Characterisation of the Microbiota Composition of STGG Storage Media
Matthew Hoyle
08/03/2021
This markdown file contains the script and figures used in the statistical analysis for my Infectious Diseases honours research project at the University of Edinburgh.
1 Load packages and import data
rm(list = ls())
# Load Packages
library(vegan)
library(phyloseq)
library(ggplot2)
library(plyr)
library(dplyr)
library(tibble)
library(fpc)
library(tidyr)
library(gridExtra)
library(Hmisc)
library(grid)
library(metagenomeSeq)
library(ggpubr)
library(decontam)
library(wesanderson)
library(pairwiseAdonis)
library(EnhancedVolcano)
library(FSA)
library(xtable)
library(forcats)
italic <- function(x){
paste("\textit{",
x,
"}")
}2 Data Preparation
# Read in Data
ps <- readRDS("phyloseq_object.rds")
ps_filtered_old <- readRDS("ps_filtered.rds")
ps_filtered <- readRDS("ps_filtered_march.rds")
ps## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 837 taxa and 56 samples ]
## sample_data() Sample Data: [ 56 samples by 8 sample variables ]
## tax_table() Taxonomy Table: [ 837 taxa by 7 taxonomic ranks ]
## refseq() DNAStringSet: [ 837 reference sequences ]ps_filtered## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 249 taxa and 53 samples ]
## sample_data() Sample Data: [ 53 samples by 8 sample variables ]
## tax_table() Taxonomy Table: [ 249 taxa by 8 taxonomic ranks ]
## refseq() DNAStringSet: [ 249 reference sequences ]# remove old ASV column from updated ps, try to reassign new taxonomy table with only new ASV
tax_species <- data.frame(tax_table(ps_filtered)) %>%
select(-c(7)) %>%
rename(ASV=ASV.1)
tax_table(ps_filtered) <- as.matrix(tax_species)
head(tax_table(ps_filtered))## Taxonomy Table: [6 taxa by 7 taxonomic ranks]:
## Kingdom Phylum Class
## Tepidimonas_1 "Bacteria" "Proteobacteria" "Gammaproteobacteria"
## Curvibacter_2 "Bacteria" "Proteobacteria" "Gammaproteobacteria"
## Thermus_3 "Bacteria" "Deinococcota" "Deinococci"
## Schlegelella_4 "Bacteria" "Proteobacteria" "Gammaproteobacteria"
## Thermus_5 "Bacteria" "Deinococcota" "Deinococci"
## Caulobacter_6 "Bacteria" "Proteobacteria" "Alphaproteobacteria"
## Order Family Genus
## Tepidimonas_1 "Burkholderiales" "Comamonadaceae" "Tepidimonas"
## Curvibacter_2 "Burkholderiales" "Comamonadaceae" "Curvibacter"
## Thermus_3 "Thermales" "Thermaceae" "Thermus"
## Schlegelella_4 "Burkholderiales" "Comamonadaceae" "Schlegelella"
## Thermus_5 "Thermales" "Thermaceae" "Thermus"
## Caulobacter_6 "Caulobacterales" "Caulobacteraceae" "Caulobacter"
## ASV
## Tepidimonas_1 "Tepidimonas_1"
## Curvibacter_2 "Curvibacter_2"
## Thermus_3 "Thermus_3"
## Schlegelella_4 "Schlegelella_4"
## Thermus_5 "Thermus_5"
## Caulobacter_6 "Caulobacter_6"# visualise data
sample_names(ps)## [1] "BLANK1-2" "BLANK10-2" "BLANK11-1" "BLANK12-2" "BLANK13-1"
## [6] "BLANK2-1" "BLANK3-1" "BLANK4-1" "BLANK5-2" "BLANK6-1"
## [11] "BLANK7-2" "BLANK8-1" "BLANK9-2" "STGG1" "STGG10"
## [16] "STGG11" "STGG12" "STGG13" "STGG14" "STGG15"
## [21] "STGG16" "STGG17" "STGG18" "STGG19" "STGG2"
## [26] "STGG20" "STGG3" "STGG4" "STGG5" "STGG6"
## [31] "STGG7" "STGG8" "STGG9" "B1_E6" "B1_E7"
## [36] "B2_E3" "B2_E4" "B2_E5" "Blank_523_1a" "Blank_523_1b"
## [41] "B1_E8" "B1_E9" "B2_E7" "Blank_523_2a" "B1_E11"
## [46] "B2_E10" "B2_E12" "RNAprot_1_1" "RNAprot_2_1" "RNAprot_3_1"
## [51] "B1_E13" "B1_E4" "RNAprot_1_2" "RNAprot_2_2" "RNAprot_3_2"
## [56] "RNAprot_4"rank_names(ps)## [1] "Kingdom" "Phylum" "Class" "Order" "Family" "Genus" "ASV"sample_variables(ps)## [1] "Sample_type" "Ct.value" "X16s_conc_pg_ul" "Run_No"
## [5] "PicoGreen" "Extraction_date" "qPCR_date" "Extracno"# visualise data
sample_names(ps_filtered)## [1] "BLANK1-2" "BLANK10-2" "BLANK11-1" "BLANK12-2" "BLANK13-1"
## [6] "BLANK2-1" "BLANK3-1" "BLANK4-1" "BLANK5-2" "BLANK6-1"
## [11] "BLANK7-2" "BLANK8-1" "BLANK9-2" "STGG1" "STGG10"
## [16] "STGG11" "STGG12" "STGG13" "STGG14" "STGG15"
## [21] "STGG16" "STGG17" "STGG18" "STGG19" "STGG2"
## [26] "STGG20" "STGG3" "STGG4" "STGG5" "STGG6"
## [31] "STGG7" "STGG8" "STGG9" "B1_E6" "B1_E7"
## [36] "B2_E3" "B2_E4" "B2_E5" "B1_E8" "B1_E9"
## [41] "B2_E7" "B1_E11" "B2_E10" "B2_E12" "RNAprot_1_1"
## [46] "RNAprot_2_1" "RNAprot_3_1" "B1_E13" "B1_E4" "RNAprot_1_2"
## [51] "RNAprot_2_2" "RNAprot_3_2" "RNAprot_4"rank_names(ps_filtered)## [1] "Kingdom" "Phylum" "Class" "Order" "Family" "Genus" "ASV"sample_variables(ps_filtered)## [1] "Sample_type" "Ct.value" "X16s_conc_pg_ul" "Run_No"
## [5] "PicoGreen" "Extraction_date" "qPCR_date" "Extracno"#Remove PCR_blanks from PS
ps <- subset_samples(ps, !(Sample_type %in% "pcr_blank"))
#Extract meta, taxa and OTU data for Future statistical analysis
metadata <- data.frame(sample_data(ps))
metadata_filtered <- data.frame(sample_data(ps_filtered))
otu_filtered <- data.frame(otu_table(ps_filtered))
tax_filtered <- data.frame(tax_table(ps_filtered))
# Number of samples in each sample type
plyr::count(metadata_filtered, "Sample_type")## Sample_type freq
## 1 blank 26
## 2 rna_protect 7
## 3 stgg 203 Alpha Diversity
3.1 Visualise a range of alpha diversity measures and choose appropriate measure
# Visualise Alpha-diversity
plot_richness(ps, x="Sample_type") +
geom_boxplot()## Warning in estimate_richness(physeq, split = TRUE, measures = measures): The data you have provided does not have
## any singletons. This is highly suspicious. Results of richness
## estimates (for example) are probably unreliable, or wrong, if you have already
## trimmed low-abundance taxa from the data.
##
## We recommended that you find the un-trimmed data and retry.
plot_richness(ps, measures = c("Observed", "Shannon"), x="Sample_type")+
geom_boxplot()## Warning in estimate_richness(physeq, split = TRUE, measures = measures): The data you have provided does not have
## any singletons. This is highly suspicious. Results of richness
## estimates (for example) are probably unreliable, or wrong, if you have already
## trimmed low-abundance taxa from the data.
##
## We recommended that you find the un-trimmed data and retry.
3.2 Create matrix of alpha diversity measures
# data frame containing a number of alpha diversity estimates using "estimate_richness()"
rich = estimate_richness(ps)## Warning in estimate_richness(ps): The data you have provided does not have
## any singletons. This is highly suspicious. Results of richness
## estimates (for example) are probably unreliable, or wrong, if you have already
## trimmed low-abundance taxa from the data.
##
## We recommended that you find the un-trimmed data and retry.3.3 Plot Observed Alpha Diversity
#Assign Observed index to Sample data column
ps@sam_data$observed_index <- rich$Observed
#Plot Observed Index
ggplot(ps@sam_data, aes(x=Sample_type, y=observed_index, fill=Sample_type))+
stat_boxplot(geom = "errorbar", width = 0.25)+
geom_boxplot()+
scale_fill_manual(name = "Sample Type", labels = c("Blank", "RNA protect", "STGG"), values = wes_palette(n=3, name = "FantasticFox1"))+
scale_x_discrete(label=c("Blank", "RNA protect", "STGG"))+
#geom_dotplot(binaxis = 'y', stackdir='center', dotsize = 1)+
geom_jitter(shape=16, position=position_jitter(0.2))+
xlab("Sample Type")+
ylab("Alpha Diversity (Observed)")+
#ggtitle("Alpha Diversity (Observed) of STGG, RNA Protect and Environmental Blank Samples") +
theme_bw()+
ggsave("Alpha_Diversity_Observed_BP.pdf", units="in", width=8, height=5, dpi=300)
Alpha diversity, as measured by the number of species observed in each sample, appears relatively similar between samples from RNA Protect storage media and environmental controls, but elevated in STGG samples.
3.4 Determine statistical significance of these differences
#Assess distribution of Observed index values
hist(rich$Observed)
#Data is normally distributed so one-way ANOVA can be used (extracted df required)
alpha_df <- data.frame(sample_data(ps))
m1 <- aov(observed_index~Sample_type, alpha_df)
plot(m1)
summary(m1)## Df Sum Sq Mean Sq F value Pr(>F)
## Sample_type 2 6576 3288 9.273 0.000376 ***
## Residuals 50 17729 355
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Perform tukey HSD post Hoc test to identify difference between groups
m1.tukey <- TukeyHSD(m1, conf.level = 0.95)
m1.tukey## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = observed_index ~ Sample_type, data = alpha_df)
##
## $Sample_type
## diff lwr upr p adj
## rna_protect-blank 4.445055 -14.9220887 23.81220 0.8447139
## stgg-blank 23.730769 10.2031447 37.25839 0.0002823
## stgg-rna_protect 19.285714 -0.6881531 39.25958 0.0604822Further analysis was preformed to determine whether the differences indicated by the boxplot were statistically significant. Observed alpha diversity data was normally distributed so one-way ANOVA was used. Results showed a significant difference in alpha diversity between sample types (p-value <0.05) and so the null hypothesis that observed alpha diversity dose not differ significantly between samples was rejected. Post-hoc analysis using a Tukey’s honestly significant difference (HSD) test supported a significant difference between STGG samples and environmental controls, whilst also indicating a small, statistically insignificant, difference between STGG and RNa Protect sample (p-value=0.06).
3.5 Plot Shannon Index
#Assign Shannon index to sample data column
ps@sam_data$shannon_index <- rich$Shannon
#Plot Shannon Index
ggplot(ps@sam_data, aes(Sample_type, shannon_index, fill=Sample_type))+
stat_boxplot(geom = "errorbar", width = 0.25)+
geom_boxplot()+
scale_fill_manual(name = "Sample Type", labels = c("Blank", "RNA protect", "STGG"), values = wes_palette(n=3, name = "FantasticFox1"))+
scale_x_discrete(label=c("Blank", "RNA protect", "STGG"))+
#geom_dotplot(binaxis = 'y', stackdir='center', dotsize = 1)+
geom_jitter(shape=16, position=position_jitter(0.2))+
xlab("Sample Type")+
ylab("Alpha Diversity (Shannon Index)")+
ggtitle("Alpha Diversity (Shannon) of STGG, RNA Protect and Environmental Controls")+
ggsave("Alpha_Diversity_Shannon_BP.pdf", units="in", width=8, height=5, dpi=300)
Alpha diversity was also assessed using the Shannon-Weiner diversity index. A boxplot indicated that shannon index was similar in all sample types.
#Assess distribution of data
hist(rich$Shannon)
ggdensity(rich$Shannon,
main = "Density of Shannon Index",
xlab = "Shannon Index")
ggqqplot(rich$Shannon)
shapiro.test(rich$Shannon)##
## Shapiro-Wilk normality test
##
## data: rich$Shannon
## W = 0.92555, p-value = 0.002717#Log Transform data to normal distribution
ps@sam_data$logShannon <- log10(max(rich$Shannon+1) - rich$Shannon)
hist(ps@sam_data$logShannon)
#Plot Shannon Index
ggplot(ps@sam_data, aes(x=Sample_type, y=logShannon, fill=Sample_type))+
stat_boxplot(geom = "errorbar", width = 0.25)+
geom_boxplot()+
scale_fill_manual(name = "Sample Type", labels = c("Blank", "RNA protect", "STGG"), values = wes_palette(n=3, name = "FantasticFox1"))+
scale_x_discrete(label=c("Blank", "RNA protect", "STGG"))+
#geom_dotplot(binaxis = 'y', stackdir='center', dotsize = 1)+
geom_jitter(shape=16, position=position_jitter(0.2))+
xlab("Sample Type")+
ylab("log10(Shannon Index)")+
#ggtitle("Alpha Diversity (Shannon) of STGG, RNA Protect and Environmental Controls")+
theme_bw()+
ggsave("Alpha_Diversity_Shannon_BP.pdf", units="in", width=8, height=5, dpi=300)
#Data appears normally distributed, so one-way ANOVA can be used (extract df)
alpha_df <- data.frame(sample_data(ps))
m2 <- aov(logShannon ~ Sample_type, alpha_df)
plot(m2)
summary(m2)## Df Sum Sq Mean Sq F value Pr(>F)
## Sample_type 2 0.0002 0.000076 0.006 0.994
## Residuals 50 0.5845 0.011690Visualization of the Shannon index data showed that a log transformation was required to achieve normal distribution. One-way ANOVA was then performed using the transformed data. Results confirmed that Shannon diversity did not differ significantly between sample types (p-value = 0.99).
4 Beta Diversity
4.1 Calulate Bray-curtis dissimilarity measure to compare between sample diversity
#Calculate Bray-curtis dissimilarity matrix to determine between sample diversity
BrayDist <- distance(ps_filtered, method = "bray")4.2 Ordination Plot with Elipses
#set seed
set.seed(10)
#run ordination
ordBC=metaMDS(t(otu_filtered), "bray", trymax=10, k=2, trace = TRUE)## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1630296
## Run 1 stress 0.1630297
## ... Procrustes: rmse 0.0001180338 max resid 0.0005637128
## ... Similar to previous best
## Run 2 stress 0.1773396
## Run 3 stress 0.1771399
## Run 4 stress 0.1804299
## Run 5 stress 0.1686418
## Run 6 stress 0.1789631
## Run 7 stress 0.1680866
## Run 8 stress 0.1785171
## Run 9 stress 0.1749398
## Run 10 stress 0.1715284
## Run 11 stress 0.176384
## Run 12 stress 0.1694593
## Run 13 stress 0.1809399
## Run 14 stress 0.171529
## Run 15 stress 0.1630296
## ... New best solution
## ... Procrustes: rmse 3.545775e-05 max resid 0.0001299901
## ... Similar to previous best
## Run 16 stress 0.1641474
## Run 17 stress 0.1726666
## Run 18 stress 0.1696494
## Run 19 stress 0.1640934
## Run 20 stress 0.1630348
## ... Procrustes: rmse 0.001221239 max resid 0.006546905
## ... Similar to previous best
## *** Solution reached#plot samples as points in a 2D space and draw elypses around each group according to "group"
nMDS <- data.frame(NMDS1=ordBC$points[,1], NMDS2=ordBC$points[,2], group= as.factor(metadata_filtered$Sample_type), MiSeq_Run= as.factor(metadata_filtered$Run_No))
df_ellipse <- data.frame()
veganCovEllipse<-function (cov, center = c(0, 0), scale = 1, npoints = 100)
{
theta <- (0:npoints) * 2 * pi/npoints
Circle <- cbind(cos(theta), sin(theta))
t(center + scale * t(Circle %*% chol(cov)))
}
for(i in 1:length(levels(nMDS$group))) {
df_ellipse <- rbind(df_ellipse, cbind(as.data.frame(with(nMDS[nMDS$group==levels(nMDS$group)[i],],
veganCovEllipse(cov.wt(cbind(NMDS1,NMDS2),wt=rep(1/length(NMDS1),length(NMDS1)))$cov, center=c(mean(NMDS1),mean(NMDS2))))),group=levels(nMDS$group)[i]))
}
ggplot(data=nMDS, aes(NMDS1, NMDS2)) +
geom_polygon(data=df_ellipse, aes(x=NMDS1, y=NMDS2, fill=group), alpha=.5,show.legend = F) +
geom_point(aes(colour = group, shape= MiSeq_Run), size=2) +
scale_color_discrete(name="Sample Type", labels=c("Blank", "RNA Protect", "STGG"))+
scale_shape_discrete(name = "Sequencing Run")
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## - attr(*, "complete")= logi TRUE
## - attr(*, "validate")= logi TRUE #ggsave("Ordination_Plot_with_Elypses.pdf", units="in", width=8, height=5, dpi=300)To investigate whether the diversity between samples varied with sample type, beta diversity was calculated using the Bray-Curtis dissimilarity measure. To visualise this highly dimensional data, non-metric multidimensional scaling (nMDS) was used to plot the samples in a two dimensional space with elypses around each sample type. Data points were also coloured according to Sample Type and assigned a shape in relation to the MiSeq run as shown in the legend. Ordination showed a dissimilarity in beta diversity between STGG and control samples. RNA Samples also appeared to differ with STGG samples, however appeared similar to control samples.
A strong distinction between samples from each MiSeq run was also observed, with samples from the same run appearing similar, whilst samples from different runs showing a strong dissimilarity.
4.3 Determine Statistical Significance using PERMANOVA
#Use Permanova to determine any statistical differences in beta diversity between sample_types
perm1 <- adonis(BrayDist ~ Sample_type + Run_No, data = metadata_filtered)
perm1##
## Call:
## adonis(formula = BrayDist ~ Sample_type + Run_No, data = metadata_filtered)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Sample_type 2 5.1014 2.5507 13.246 0.28228 0.001 ***
## Run_No 1 3.5349 3.5349 18.357 0.19560 0.001 ***
## Residuals 49 9.4358 0.1926 0.52212
## Total 52 18.0720 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print(xtable(perm1$aov.tab, type = "latex", digits = 3), file = "Beta_PERMANOVA1.tex")
perm2 <- adonis(BrayDist ~ Run_No, data = metadata_filtered, )
perm2##
## Call:
## adonis(formula = BrayDist ~ Run_No, data = metadata_filtered)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Run_No 1 6.1064 6.1064 26.027 0.33789 0.001 ***
## Residuals 51 11.9657 0.2346 0.66211
## Total 52 18.0720 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Perform post-hoc analysis to determine between which groups is this difference is occurring
library(devtools)
#install_github("pmartinezarbizu/pairwiseAdonis/pairwiseAdonis")
perm1.2 <- pairwise.adonis(BrayDist, factors = metadata_filtered$Sample_type, p.adjust.m = 'holm')
perm1.2## pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
## 1 blank vs stgg 1 3.7369290 14.111591 0.24283608 0.001 0.003 *
## 2 blank vs rna_protect 1 0.6168048 1.898596 0.05771053 0.080 0.080
## 3 stgg vs rna_protect 1 2.8932695 17.146252 0.40682744 0.001 0.003 *print(xtable(perm1.2, type = "latex", digits = 3), file = "Beta_PERMANOVA1.2.tex")These observations were supported by results from permutational multivariate analysis of variance (PERMANOVA). The dissimilarity between groups was shown to be statistically significant (p-value<0.05), and post-hoc analysis with pairwiseAdonis showed this dissimilarity between Blank and STGG samples (adjusted p-value<0.05) but not between Blank and RNA Protect samples (adjusted p-value<0.05). Results also showed a significant dissimilarity between STGG and RNA Protect Samples (adjusted p-value<0.05).
A significant difference between samples from MiSeq run 514 and 523 was also observed (p-value<0.05).
4.4 Beta Diversity of each sample type in Run_No 523
As the NMDS plot had shown a significant difference in beta diversity between MiSeq runs, further analysis was conducted to investigate how beta diversity compared between sample types in each run.
ps_filtered523 <- subset_samples(ps_filtered, !(Run_No %in% "514"))
ps_filtered523 <- prune_taxa(taxa_sums(ps_filtered523) > 0, ps_filtered523)
#Calculate Bray-curtis dissimilarity matrix to determine between sample diversity
BrayDist523 <- distance(ps_filtered523, method = "bray")
#Extract Meta data and OTU
metadata523 <- data.frame(sample_data(ps_filtered523))
otu_523 <- data.frame(otu_table(ps_filtered523))
#set seed
set.seed(10)
#run ordination
ordBC523=metaMDS(t(otu_523), "bray", trymax=10, k=2, trace = TRUE)## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1063271
## Run 1 stress 0.1057221
## ... New best solution
## ... Procrustes: rmse 0.04905362 max resid 0.1079511
## Run 2 stress 0.1081985
## Run 3 stress 0.1073357
## Run 4 stress 0.1058492
## ... Procrustes: rmse 0.04373376 max resid 0.1024493
## Run 5 stress 0.1056089
## ... New best solution
## ... Procrustes: rmse 0.02119661 max resid 0.07130839
## Run 6 stress 0.1066134
## Run 7 stress 0.1081577
## Run 8 stress 0.1056088
## ... New best solution
## ... Procrustes: rmse 0.0001494597 max resid 0.0005017097
## ... Similar to previous best
## Run 9 stress 0.1059444
## ... Procrustes: rmse 0.05056411 max resid 0.1084485
## Run 10 stress 0.1073359
## Run 11 stress 0.1059451
## ... Procrustes: rmse 0.05054034 max resid 0.1083398
## Run 12 stress 0.1056088
## ... New best solution
## ... Procrustes: rmse 0.0001216241 max resid 0.0002212386
## ... Similar to previous best
## Run 13 stress 0.105849
## ... Procrustes: rmse 0.05047258 max resid 0.1031852
## Run 14 stress 0.1059445
## ... Procrustes: rmse 0.05056571 max resid 0.1084993
## Run 15 stress 0.1064428
## Run 16 stress 0.1059507
## ... Procrustes: rmse 0.05044598 max resid 0.1083006
## Run 17 stress 0.1057258
## ... Procrustes: rmse 0.04779156 max resid 0.1080937
## Run 18 stress 0.1066189
## Run 19 stress 0.1057997
## ... Procrustes: rmse 0.01999571 max resid 0.05825011
## Run 20 stress 0.1063271
## *** Solution reached#plot samples as points in a 2D space and draw elypses around each group according to "group"
nMDS523 <- data.frame(NMDS1=ordBC523$points[,1], NMDS2=ordBC523$points[,2], group= as.factor(metadata523$Sample_type))
df_ellipse <- data.frame()
veganCovEllipse<-function (cov, center = c(0, 0), scale = 1, npoints = 100)
{
theta <- (0:npoints) * 2 * pi/npoints
Circle <- cbind(cos(theta), sin(theta))
t(center + scale * t(Circle %*% chol(cov)))
}
for(i in 1:length(levels(nMDS523$group))) {
df_ellipse <- rbind(df_ellipse, cbind(as.data.frame(with(nMDS523[nMDS523$group==levels(nMDS523$group)[i],],
veganCovEllipse(cov.wt(cbind(NMDS1,NMDS2),wt=rep(1/length(NMDS1),length(NMDS1)))$cov, center=c(mean(NMDS1),mean(NMDS2))))),group=levels(nMDS$group)[i]))
}
ggplot(data=nMDS523, aes(NMDS1, NMDS2)) +
geom_polygon(data=df_ellipse, aes(x=NMDS1, y=NMDS2, fill=group), alpha=.5,show.legend = F) +
geom_point(aes(colour = group), size=2) +
scale_color_discrete(name="Sample Type", labels=c("Blank", "RNA Protect", "STGG"))+
theme_bw()+
ggsave("Ordination_Plot_with_Elypses_Run523.pdf", units="in", width=5, height=3, dpi=300)
#PERMANOVA to check statistical significance
perm523 <- adonis(BrayDist523 ~ Sample_type, data = metadata523)
perm523##
## Call:
## adonis(formula = BrayDist523 ~ Sample_type, data = metadata523)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Sample_type 1 0.3025 0.30254 1.8856 0.09482 0.083 .
## Residuals 18 2.8881 0.16045 0.90518
## Total 19 3.1906 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print(xtable(perm523$aov.tab, type = "latex", digits = 3), file = "Beta_PERMANOVA523.tex")The Bray-Curtis dissimilarity measure of RNA protect and control samples from MiSeq run no. 523 was calculated and visualised using an NMDS plot. Results showed no dissimilary between RNA protect samples and environmental control blanks, which was supported by PERMANOVA results (p-value<0.05).
4.5 Beta Diversity of each sample type in Run_No 514
ps_filtered514 <- subset_samples(ps_filtered, !(Run_No %in% "523"))
ps_filtered514 <- prune_taxa(taxa_sums(ps_filtered514) > 0, ps_filtered514)
#Calculate Bray-curtis dissimilarity matrix to determine between sample diversity
BrayDist514 <- distance(ps_filtered514, method = "bray")
#Extract Meta data and OTU
metadata514 <- data.frame(sample_data(ps_filtered514))
otu_514 <- data.frame(otu_table(ps_filtered514))
#set seed
set.seed(10)
#run ordination
ordBC514=metaMDS(t(otu_514), "bray", trymax=10, k=2, trace = TRUE)## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.2181672
## Run 1 stress 0.2181976
## ... Procrustes: rmse 0.1312561 max resid 0.3305768
## Run 2 stress 0.2176561
## ... New best solution
## ... Procrustes: rmse 0.03935621 max resid 0.1319125
## Run 3 stress 0.2329944
## Run 4 stress 0.2219854
## Run 5 stress 0.2300727
## Run 6 stress 0.2249456
## Run 7 stress 0.2205169
## Run 8 stress 0.2182917
## Run 9 stress 0.2369317
## Run 10 stress 0.2211973
## Run 11 stress 0.217572
## ... New best solution
## ... Procrustes: rmse 0.01432089 max resid 0.05912974
## Run 12 stress 0.2181978
## Run 13 stress 0.2176566
## ... Procrustes: rmse 0.0144357 max resid 0.06171741
## Run 14 stress 0.2234542
## Run 15 stress 0.2161268
## ... New best solution
## ... Procrustes: rmse 0.09848959 max resid 0.4126196
## Run 16 stress 0.2312062
## Run 17 stress 0.2182954
## Run 18 stress 0.2214521
## Run 19 stress 0.2205494
## Run 20 stress 0.2190393
## *** No convergence -- monoMDS stopping criteria:
## 1: no. of iterations >= maxit
## 19: stress ratio > sratmax#plot samples as points in a 2D space and draw elypses around each group according to "group"
nMDS514 <- data.frame(NMDS1=ordBC514$points[,1], NMDS2=ordBC514$points[,2], group= as.factor(metadata514$Sample_type))
df_ellipse <- data.frame()
veganCovEllipse<-function (cov, center = c(0, 0), scale = 1, npoints = 100)
{
theta <- (0:npoints) * 2 * pi/npoints
Circle <- cbind(cos(theta), sin(theta))
t(center + scale * t(Circle %*% chol(cov)))
}
for(i in 1:length(levels(nMDS514$group))) {
df_ellipse <- rbind(df_ellipse, cbind(as.data.frame(with(nMDS514[nMDS514$group==levels(nMDS514$group)[i],],
veganCovEllipse(cov.wt(cbind(NMDS1,NMDS2),wt=rep(1/length(NMDS1),length(NMDS1)))$cov, center=c(mean(NMDS1),mean(NMDS2))))),group=levels(nMDS$group)[i]))
}
ggplot(data=nMDS514, aes(NMDS1, NMDS2)) +
geom_polygon(data=df_ellipse, aes(x=NMDS1, y=NMDS2, fill=group), alpha=.5,show.legend = F) +
geom_point(aes(colour = group), size=2) +
scale_color_discrete(name="Sample Type", labels=c("Blank", "STGG"))+
theme_bw()+
ggsave("Ordination_Plot_with_Elypses_Run514.pdf", units="in", width=5, height=3, dpi=300)
#PERMANOVA to check statistical significance
perm514 <- adonis(BrayDist514 ~ Sample_type, data = metadata514)
perm514##
## Call:
## adonis(formula = BrayDist514 ~ Sample_type, data = metadata514)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Sample_type 1 2.2273 2.22729 10.545 0.25382 0.001 ***
## Residuals 31 6.5477 0.21122 0.74618
## Total 32 8.7750 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print(xtable(perm514$aov.tab, type = "latex", digits = 3), file = "Beta_PERMANOVA514.tex")The Bray-Curtis dissimilarity measure of STGG and control samples from MiSeq run no. 514 was calculated and visualised using an NMDS plot. Results showed dissimilarity between RNA protect samples and environmental control blanks. PERMANOVA found this dissimilarity to be statistically significant (p-value<0.05).
4.6 Beta Diversity of Enivronmental Blanks
ps_filtered_blanks <- ps_filtered %>%
subset_samples(!(Sample_type %in% "stgg")) %>%
subset_samples(!(Sample_type %in% "rna_protect"))
#Calculate Bray-curtis dissimilarity matrix to determine between sample diversity
BrayDist_blanks <- distance(ps_filtered_blanks, method = "bray")
#Extract Meta data and OTU
metadata_blanks <- data.frame(sample_data(ps_filtered_blanks))
otu_blanks <- data.frame(otu_table(ps_filtered_blanks))
#set seed
set.seed(10)
#run ordination
ordBC_blanks=metaMDS(t(otu_blanks), "bray", trymax=10, k=2, trace = TRUE)## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1014184
## Run 1 stress 0.1014184
## ... Procrustes: rmse 3.378e-06 max resid 1.208614e-05
## ... Similar to previous best
## Run 2 stress 0.1015034
## ... Procrustes: rmse 0.00231414 max resid 0.008759448
## ... Similar to previous best
## Run 3 stress 0.1068923
## Run 4 stress 0.1017015
## ... Procrustes: rmse 0.007532322 max resid 0.02699233
## Run 5 stress 0.1023448
## Run 6 stress 0.1273847
## Run 7 stress 0.1145828
## Run 8 stress 0.1014184
## ... Procrustes: rmse 3.603125e-06 max resid 8.917658e-06
## ... Similar to previous best
## Run 9 stress 0.1134541
## Run 10 stress 0.1072017
## Run 11 stress 0.1146329
## Run 12 stress 0.1069779
## Run 13 stress 0.1023868
## Run 14 stress 0.1014184
## ... Procrustes: rmse 4.2256e-06 max resid 1.143086e-05
## ... Similar to previous best
## Run 15 stress 0.124595
## Run 16 stress 0.1068536
## Run 17 stress 0.1023868
## Run 18 stress 0.1135888
## Run 19 stress 0.1014184
## ... Procrustes: rmse 1.125122e-05 max resid 3.534936e-05
## ... Similar to previous best
## Run 20 stress 0.121181
## *** Solution reached#plot samples as points in a 2D space and draw elypses around each group according to "group"
nMDS_blanks <- data.frame(NMDS1=ordBC_blanks$points[,1], NMDS2=ordBC_blanks$points[,2], group= as.factor(metadata_blanks$Run_No))
df_ellipse <- data.frame()
veganCovEllipse<-function (cov, center = c(0, 0), scale = 1, npoints = 100)
{
theta <- (0:npoints) * 2 * pi/npoints
Circle <- cbind(cos(theta), sin(theta))
t(center + scale * t(Circle %*% chol(cov)))
}
for(i in 1:length(levels(nMDS_blanks$group))) {
df_ellipse <- rbind(df_ellipse, cbind(as.data.frame(with(nMDS_blanks[nMDS_blanks$group==levels(nMDS_blanks$group)[i],],
veganCovEllipse(cov.wt(cbind(NMDS1,NMDS2),wt=rep(1/length(NMDS1),length(NMDS1)))$cov, center=c(mean(NMDS1),mean(NMDS2))))),group=levels(nMDS$group)[i]))
}
ggplot(data=nMDS_blanks, aes(NMDS1, NMDS2)) +
geom_polygon(data=df_ellipse, aes(x=NMDS1, y=NMDS2, fill=group), alpha=.5,show.legend = F) +
geom_point(aes(colour = group), size=2) +
scale_color_discrete(name="MiSeq Run No.", labels=c("514", "523"))+
ggsave("Ordination_Plot_with_Elypses_Blanks.tiff", units="in", width=8, height=5, dpi=300, compression = 'lzw')
#PERMANOVA to check statistical significance
perm_blanks <- adonis(BrayDist_blanks ~ Run_No, data = metadata_blanks)
perm_blanks##
## Call:
## adonis(formula = BrayDist_blanks ~ Run_No, data = metadata_blanks)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Run_No 1 3.5349 3.5349 16.261 0.40388 0.001 ***
## Residuals 24 5.2173 0.2174 0.59612
## Total 25 8.7522 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 15 Stacked Bar Chart
5.1 First plot relative abundances at Phylum level
###phylum level relative abundances
phy_phylum<-tax_glom(ps_filtered, "Phylum") ##10
#Extract OTUs at phylumlevel
otu_phy<-phy_phylum%>%
otu_table%>%
as("matrix")%>%
magrittr::set_rownames(paste(tax_table(phy_phylum)[,"Phylum"], seq(1:length(tax_table(phy_phylum)[,"Phylum"])), sep = "_"))%>%
data.frame(.)
##Convert into RA
otu_phy_RA <- t(t(otu_phy)/colSums(otu_phy))
phy_table_ord <- otu_phy_RA[order(rowSums(otu_phy),decreasing=T),]
n = 10
stack.dfT_phy <- cbind(data.frame(Sample.ID=rownames(metadata_filtered)), t(phy_table_ord))%>%
data.frame(check.names = F)%>%
gather(key=Phylum, value=RA, -Sample.ID) %>%
mutate(ASV = factor(Phylum, levels = rev(unique(Phylum))))
metadata_filtered$Sample <-rownames(metadata_filtered)
#Add Sample_type as a variable of interest
Sample_group<-metadata_filtered%>%
select(Sample, Sample_type) %>%
deframe()
stack.dfT_phy$Sample_group <- Sample_group[match(stack.dfT_phy$Sample.ID, names(Sample_group))]
##overall mean abundances ,
mean_ab_groups_phy <- stack.dfT_phy%>%
group_by(Phylum)%>%
summarise(mean=mean(RA))%>%
mutate(mean_percentage=mean*100)%>%
arrange(desc(mean_percentage))
write.table(mean_ab_groups_phy, file="mean_abundances_phylum_level_all.txt")
print(xtable(mean_ab_groups_phy, digits = 4, type = "latex"), file = "mean_abundances_phylum_level.tex")
##RA stratified by Group
mean_ab_groups_phy_str<-stack.dfT_phy%>%
group_by(ASV,Sample_group)%>%
summarise(mean=mean(RA))%>%
mutate(mean_percentage=mean*100,Group=ifelse(Sample_group=="blank","STGG","rna_protect"))
#color vector
col <-c("#0095e9", "#ffd500","#b452f6","#83cf00",
"#d84e9a",
"#01c343",
"#ffacfe",
"#01ad6b",
"#ffb539",
"#cedeff",
"#a98c00",
"#688794",
"#fffe90",
"#b66e72",
"#b7ffe2",
"#5e8f47",
"#ffeeb4")
#Create facet labels
facet_labels <- c("Blank", "RNA Protect", "STGG")
##Plot Stack Bargraph
ggplot(mean_ab_groups_phy_str, aes(x = Sample_group, y = mean_percentage, fill = ASV)) +
geom_bar(stat = "identity", position = "stack") +
ylab("Relative Abundance (%)")+
xlab("Sample Type")+
scale_fill_manual(values = col)+
#facet_grid(.~Sample_group, scales = "free", labeller = labeller("Blank", "RNA Protect", "STGG"))+
scale_x_discrete(label=c("Blank", "RNA protect", "STGG"))+
theme_bw() +
#theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5)) +
ggsave("Relative_abundances_phylum_sample_type.pdf", units="in", width=8, height=5, dpi=300)
##RA stratified by Group
mean_ab_groups_phy_sample<-stack.dfT_phy%>%
group_by(ASV,Sample_group, Sample.ID)%>%
summarise(mean=mean(RA))%>%
mutate(mean_percentage=mean*100,Group=ifelse(Sample_group=="blank","STGG","rna_protect"))
##Plot Stack Bargraph
ggplot(mean_ab_groups_phy_sample, aes(x = Sample.ID, y = mean_percentage, fill = ASV)) +
geom_bar(stat = "identity", position = "stack") +
ylab("Relative Abundance (%)")+
xlab("Sample Type")+
scale_fill_manual(values = col)+
#facet_grid(.~Sample_group, scales = "free", labeller = labeller("Blank", "RNA Protect", "STGG"))+
theme_bw() +
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5)) +
ggsave("Relative_abundances_phylum_sample.pdf", units="in", width=8, height=5, dpi=300)
A stacked bar chart was created to visualise the microbial composition of each sample type at the phylum level. Phyla are shown in relative abundance, as a percentage of the mean total counts in each sample type. At phylum level the microbial composition of each sample type appears relatively similar. Proteobacteria are the most abundant phyla in all sample types accounting for around 75% (73.96%) of microbial sequences detected in each sample. Deinococotta was the second most abundant present with an average relative abundance of around 15% (16.3%) in all sample types. Actinobateriota and Firmicutes accounted for the majority of the remaining taxa in all sample types, with average relative abundances of approximately 5% (4.64%) and 4% (4.22%).
5.1.1 Relative Abundance
RA.df_phy <- stack.dfT_phy%>%
mutate(log10_RA=log10(RA+1))%>%
mutate(sqrt_RA=sqrt(RA))
MiSeq_Run<-metadata_filtered%>%
select(Sample, Run_No) %>%
deframe()
RA.df_phy$MiSeq_Run<-MiSeq_Run[match(RA.df_phy$Sample.ID, names(MiSeq_Run))]5.1.1.1 Proteobacteria
ProRA <- filter(RA.df_phy, ASV=="Proteobacteria_1")
Pro1 <- lm(RA ~ Sample_group, data=ProRA)
summary(Pro1)##
## Call:
## lm(formula = RA ~ Sample_group, data = ProRA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.36812 -0.02732 -0.00449 0.03719 0.26000
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.74000 0.01957 37.821 <2e-16 ***
## Sample_grouprna_protect -0.01309 0.04248 -0.308 0.759
## Sample_groupstgg 0.01331 0.02967 0.449 0.656
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.09977 on 50 degrees of freedom
## Multiple R-squared: 0.008304, Adjusted R-squared: -0.03136
## F-statistic: 0.2093 on 2 and 50 DF, p-value: 0.8118par(mfrow=c(2,2))
plot(Pro1)
#### Deinococcota_2
DeinRA <- filter(RA.df_phy, ASV=="Deinococcota_2")
Dein2 <- lm(RA ~ Sample_group, data=DeinRA)
summary(Dein2)##
## Call:
## lm(formula = RA ~ Sample_group, data = DeinRA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.155264 -0.049418 0.004759 0.047313 0.261403
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.155264 0.016436 9.447 1.05e-12 ***
## Sample_grouprna_protect 0.033338 0.035686 0.934 0.355
## Sample_groupstgg -0.009041 0.024926 -0.363 0.718
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.08381 on 50 degrees of freedom
## Multiple R-squared: 0.02597, Adjusted R-squared: -0.01299
## F-statistic: 0.6665 on 2 and 50 DF, p-value: 0.518par(mfrow=c(2,2))
plot(Dein2)
#### Firmicutes_3
FirmRA <- filter(RA.df_phy, ASV=="Firmicutes_3")
Firm3 <- lm(RA ~ Sample_group, data=FirmRA)
summary(Firm3)##
## Call:
## lm(formula = RA ~ Sample_group, data = FirmRA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.046449 -0.034023 -0.019276 0.001527 0.250426
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0464494 0.0117213 3.963 0.000236 ***
## Sample_grouprna_protect -0.0118009 0.0254499 -0.464 0.644880
## Sample_groupstgg -0.0008598 0.0177763 -0.048 0.961617
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.05977 on 50 degrees of freedom
## Multiple R-squared: 0.004468, Adjusted R-squared: -0.03535
## F-statistic: 0.1122 on 2 and 50 DF, p-value: 0.8941par(mfrow=c(2,2))
plot(Firm3)
#### Actinobacteriota_4
ActinRA <- filter(RA.df_phy, ASV=="Actinobacteriota_4")
Actin4 <- lm(RA ~ Sample_group, data=ActinRA)
summary(Actin4)##
## Call:
## lm(formula = RA ~ Sample_group, data = ActinRA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.052625 -0.036702 -0.009335 0.008342 0.206750
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.052625 0.011053 4.761 1.69e-05 ***
## Sample_grouprna_protect -0.015194 0.023999 -0.633 0.530
## Sample_groupstgg -0.003387 0.016763 -0.202 0.841
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.05636 on 50 degrees of freedom
## Multiple R-squared: 0.007955, Adjusted R-squared: -0.03173
## F-statistic: 0.2005 on 2 and 50 DF, p-value: 0.819par(mfrow=c(2,2))
plot(Actin4)
#### Bacteroidota_6
BacterRA <- filter(RA.df_phy, ASV=="Actinobacteriota_4")
Bacter6 <- lm(RA ~ Sample_group, data=BacterRA)
summary(Bacter6)##
## Call:
## lm(formula = RA ~ Sample_group, data = BacterRA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.052625 -0.036702 -0.009335 0.008342 0.206750
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.052625 0.011053 4.761 1.69e-05 ***
## Sample_grouprna_protect -0.015194 0.023999 -0.633 0.530
## Sample_groupstgg -0.003387 0.016763 -0.202 0.841
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.05636 on 50 degrees of freedom
## Multiple R-squared: 0.007955, Adjusted R-squared: -0.03173
## F-statistic: 0.2005 on 2 and 50 DF, p-value: 0.819par(mfrow=c(2,2))
plot(Bacter6) #### Bdellovibrionota_5
BdellRA <- filter(RA.df_phy, ASV=="Bdellovibrionota_5")
Bdell5 <- lm(RA ~ Sample_group, data=BdellRA)
summary(Bdell5)##
## Call:
## lm(formula = RA ~ Sample_group, data = BdellRA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.007736 -0.003669 0.000000 0.000000 0.015081
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.003669 0.001012 3.624 0.000678 ***
## Sample_grouprna_protect 0.004067 0.002198 1.850 0.070167 .
## Sample_groupstgg -0.003669 0.001535 -2.390 0.020674 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.005162 on 50 degrees of freedom
## Multiple R-squared: 0.2066, Adjusted R-squared: 0.1749
## F-statistic: 6.511 on 2 and 50 DF, p-value: 0.003069par(mfrow=c(2,2))
plot(Bdell5)
#### Myxococcota_7
myxlRA <- filter(RA.df_phy, ASV=="Myxococcota_7")
myxl <- lm(RA ~ Sample_group, data=myxlRA)
summary(myxl)##
## Call:
## lm(formula = RA ~ Sample_group, data = myxlRA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0005202 -0.0005202 0.0000000 0.0000000 0.0049146
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0005202 0.0001796 2.897 0.00558 **
## Sample_grouprna_protect -0.0001479 0.0003899 -0.379 0.70610
## Sample_groupstgg -0.0005202 0.0002723 -1.910 0.06186 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0009157 on 50 degrees of freedom
## Multiple R-squared: 0.06878, Adjusted R-squared: 0.03153
## F-statistic: 1.846 on 2 and 50 DF, p-value: 0.1684par(mfrow=c(2,2))
plot(myxl)
#### Acidobacteriota_8
acidoRA <- filter(RA.df_phy, ASV=="Acidobacteriota_8")
acido <- lm(RA ~ Sample_group, data=acidoRA)
summary(acido)##
## Call:
## lm(formula = RA ~ Sample_group, data = acidoRA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0003258 -0.0001518 -0.0001518 0.0000000 0.0026583
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.518e-04 9.777e-05 1.552 0.127
## Sample_grouprna_protect 1.740e-04 2.123e-04 0.820 0.416
## Sample_groupstgg -1.518e-04 1.483e-04 -1.023 0.311
##
## Residual standard error: 0.0004985 on 50 degrees of freedom
## Multiple R-squared: 0.04681, Adjusted R-squared: 0.008687
## F-statistic: 1.228 on 2 and 50 DF, p-value: 0.3016par(mfrow=c(2,2))
plot(acido)
#### Planctomycetota_9
plancRA <- filter(RA.df_phy, ASV=="Planctomycetota_9")
planc <- lm(RA ~ Sample_group, data=plancRA)
summary(planc)##
## Call:
## lm(formula = RA ~ Sample_group, data = plancRA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0005695 -0.0005695 0.0000000 0.0000000 0.0088403
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.366e-19 2.572e-04 0.00 1.000
## Sample_grouprna_protect -1.320e-19 5.584e-04 0.00 1.000
## Sample_groupstgg 5.695e-04 3.900e-04 1.46 0.151
##
## Residual standard error: 0.001311 on 50 degrees of freedom
## Multiple R-squared: 0.04486, Adjusted R-squared: 0.006656
## F-statistic: 1.174 on 2 and 50 DF, p-value: 0.3174par(mfrow=c(2,2))
plot(planc)
#### Chloroflexi_10
chloroRA <- filter(RA.df_phy, ASV=="Chloroflexi_10")
chloro <- lm(RA ~ Sample_group, data=chloroRA)
summary(chloro)##
## Call:
## lm(formula = RA ~ Sample_group, data = chloroRA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0003593 -0.0003593 0.0000000 0.0000000 0.0056288
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.957e-20 1.634e-04 0.00 1.000
## Sample_grouprna_protect 0.000e+00 3.548e-04 0.00 1.000
## Sample_groupstgg 3.593e-04 2.478e-04 1.45 0.153
##
## Residual standard error: 0.0008332 on 50 degrees of freedom
## Multiple R-squared: 0.04426, Adjusted R-squared: 0.00603
## F-statistic: 1.158 on 2 and 50 DF, p-value: 0.3225par(mfrow=c(2,2))
plot(chloro)
#### Adjusted P-Values
lmp <- function (modelobject) {
if (class(modelobject) != "lm") stop("Not an object of class 'lm' ")
f <- summary(modelobject)$fstatistic
p <- pf(f[1],f[2],f[3],lower.tail=F)
attributes(p) <- NULL
return(p)
}
p.adjust(c("0.812", "0.518", "0.819", "0.894", "0.819", "0.003", "0.168", "0.317", "0.323", "0.302"), method = "BH")## [1] 0.8940000 0.8633333 0.8940000 0.8940000 0.8940000 0.0300000 0.6460000
## [8] 0.6460000 0.6460000 0.6460000phy_R2 <- c(summary(Pro1)$r.squared, summary(Dein2)$r.squared, summary(Actin4)$r.squared, summary(Firm3)$r.squared, summary(Bacter6)$r.squared, summary(Bdell5)$r.squared, summary(myxl)$r.squared, summary(planc)$r.squared, summary(chloro)$r.squared, summary(acido)$r.squared)
phy_pval <- c(lmp(Pro1), lmp(Dein2), lmp(Actin4), lmp(Firm3), lmp(Bacter6), lmp(Bdell5), lmp(myxl), lmp(planc), lmp(chloro), lmp(acido))
phy_adjpval <- p.adjust(phy_pval, method = "BH")
phy_names <- mean_ab_groups_phy$Phylum
phy_lm_table <- data.frame(phy_names, phy_R2, phy_pval, phy_adjpval)
print(phy_lm_table)## phy_names phy_R2 phy_pval phy_adjpval
## 1 Proteobacteria_1 0.008303948 0.811829087 0.89407770
## 2 Deinococcota_2 0.025968192 0.517998486 0.86333081
## 3 Actinobacteriota_4 0.007954793 0.819005051 0.89407770
## 4 Firmicutes_3 0.004468490 0.894077702 0.89407770
## 5 Bacteroidota_6 0.007954793 0.819005051 0.89407770
## 6 Bdellovibrionota_5 0.206617659 0.003069469 0.03069469
## 7 Myxococcota_7 0.068778686 0.168392478 0.64495541
## 8 Planctomycetota_9 0.044861754 0.317435791 0.64495541
## 9 Chloroflexi_10 0.044259504 0.322477707 0.64495541
## 10 Acidobacteriota_8 0.046814865 0.301600130 0.64495541print(xtable(phy_lm_table, type = "latex", digits = 3), file="Phylum_LM_results.tex")5.2 Number of Genera
phy_genus<-tax_glom(ps_filtered, "Genus")
phy_genus## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 136 taxa and 53 samples ]
## sample_data() Sample Data: [ 53 samples by 8 sample variables ]
## tax_table() Taxonomy Table: [ 136 taxa by 7 taxonomic ranks ]
## refseq() DNAStringSet: [ 136 reference sequences ]5.3 Plot Top 20 Most Abundant OTU/ASVs and the rest as residuals
#Top 20 otus
OTU1<- as(otu_table(ps_filtered), "matrix")
if(taxa_are_rows(ps_filtered)){OTU1 <- t(OTU1)}
OTU1.t <-t(OTU1)
otu_table_ord<-OTU1.t[order(rowSums(OTU1.t),decreasing=T),]
n = 20 # only show top 20 OTUs
otu_table_ord_RA <- t(t(otu_table_ord)/colSums(otu_table_ord))
stack.dfT <- cbind(data.frame(Sample.ID=rownames(metadata_filtered)), t(otu_table_ord_RA[1:n, ]) %>%
data.frame(check.names = F) %>%
mutate(Residuals = 1 - rowSums(.))) %>%
gather(key=ASV, value=RA, -Sample.ID) %>%
mutate(ASV = factor(ASV, levels = rev(unique(ASV))))## Warning: attributes are not identical across measure variables;
## they will be droppedmetadata_filtered$Sample <-rownames(metadata_filtered)
Sample_group<-metadata_filtered%>%
select(Sample, Sample_type) %>%
deframe()
MiSeq_Run <- metadata_filtered %>%
select(Sample, Run_No)
stack.dfT$Sample_group<-Sample_group[match(stack.dfT$Sample.ID, names(Sample_group))]
##overall mean abundances
mean_ab_groups<-stack.dfT%>%
group_by(ASV)%>%
summarise(mean=mean(RA))%>%
mutate(mean_percentage=mean*100)%>%
arrange(desc(mean_percentage))
write.table(mean_ab_groups,file="Relative_abundance_summary_Top20_OTU.txt ")
print(xtable(mean_ab_groups, type = "latex", digits = 4), file = "Relative_abundance_summary_Top20_OTU.tex")
##mean abundances stratified by Sample_type
mean_ab_groups_str<-stack.dfT%>%
group_by(ASV, Sample_group, Sample.ID)%>%
summarise(mean=mean(RA))%>%
mutate(mean_percentage=mean*100, Group=ifelse(Sample_group=="blank","STGG","rna_protect"))
cols<-c("gray75", "#d28dcb",
"#5eb847",
"#7166d9",
"#a8b635",
"#b756c0",
"#5bbe7f",
"#d74b91",
"#56802f",
"#696fba",
"#d59c34",
"#5f9ed7",
"#cb5329",
"#4cc2bc",
"#d34359",
"#39855f",
"#9d4c79",
"#acb064",
"#c06762",
"#826b2b",
"#cf8a57")
##plot
ggplot(mean_ab_groups_str, aes(x =Sample.ID, y = mean_percentage, fill = ASV)) +
geom_bar(stat = "identity", position = "stack",) +
ylab("Relative abundance(%)")+
xlab("Sample")+
scale_fill_manual(values = cols)+
#facet_grid(.~Group)+
theme_bw() +
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5), legend.position = "bottom", legend.text = element_text(face = "italic")) +
guides(fill = guide_legend(ncol = 3)) +
ggsave("Relative_abundances_top20OTUs.tiff", units="in", width=8, height=6, dpi=300, compression = 'lzw')
To examine the microbial composition of each sample at a higher taxonomic resolution, the top 20 most abundant ASVs were calculated, and their relative abundance in each sample plotted using a stacked bar chart. Tepidimonas_1 was the most abundant ASV across all samples with a mean relative abundance of 22.6%. Curvibacter_2 and Thermus_3 were the second most abundant ASVs at 10.1% and 8.7%.
Of these top 20 most abundant ASVs, the vast majority belong to water and soil associated genera that have been previously described as known contaminants found in sequenced blank controls (Salter et al., 2014). The presence of an ASV belonging to the Staphylococcus genus was also noted as bacteria belonging to this genus are often found as part of the skin microbiota but some may colonise the upper respiratory tract.
ggplot(data = mean_ab_groups, aes(x=ASV, y=mean_percentage, fill=ASV))+
geom_bar(stat="identity")+
ylab("Relative abundance (%)")+
xlab(NULL)+
scale_fill_manual(values = cols)+
#facet_grid(.~Group)+
theme_bw() +
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5), legend.position = "bottom", legend.text = element_text(face = "italic")) +
scale_x_discrete(label=NULL)+
guides(fill = guide_legend(ncol = 3))+
ggsave("Relative_abundances_top20OTUs_Bar_Chart.pdf", units="in", width=8, height=6, dpi=300)
print(mean_ab_groups)## # A tibble: 21 x 3
## ASV mean mean_percentage
## <fct> <dbl> <dbl>
## 1 Residuals 0.280 28.0
## 2 Tepidimonas_1 0.226 22.6
## 3 Curvibacter_2 0.101 10.1
## 4 Thermus_3 0.0869 8.69
## 5 Thermus_5 0.0440 4.40
## 6 Sphingomonas_8 0.0411 4.11
## 7 Schlegelella_4 0.0285 2.85
## 8 Paucibacter_19 0.0256 2.56
## 9 Methylobacterium_Methylorubrum_13 0.0229 2.29
## 10 Nitriliruptoraceae_18 0.0167 1.67
## # … with 11 more rows5.4 Plot Top20 OTU/ASVs by Sample Type
group.labs <- c("blank"="Blank", "rna_protect"="RNA protect", "stgg"="STGG")
ggplot(mean_ab_groups_str, aes(x =Sample.ID, y = mean_percentage, fill = ASV)) +
geom_bar(stat = "identity", position = "stack",) +
ylab("Relative abundance(%)")+
xlab("Sample")+
scale_fill_manual(values = cols)+
scale_x_discrete(label=NULL)+
theme_bw() +
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5), legend.position = "bottom", legend.text = element_text(face = "italic")) +
guides(fill = guide_legend(ncol = 3)) +
facet_wrap(~Sample_group, labeller = labeller(Sample_group=group.labs), scales="free", nrow = 1,) +
ggsave("Relative_abundances_top20OTUs_Sample_type.pdf", units="in", width=10, height=5, dpi=300)
Upon dividing samples into groups according to sample types a number of trends become more obvious. Firstly a strong distinction between two groups of environmental control blanks emerged. The samples within each cluster from the enironmental control sample type shared a relatively stable and uniform microbial profile, however the composition of each control blank cluster appeared markedly different.
Grouped by miseq run
RA.df <- stack.dfT%>%
mutate(log10_RA=log10(RA+1))%>%
mutate(sqrt_RA=sqrt(RA))
MiSeq_Run<-metadata_filtered%>%
select(Sample, Run_No) %>%
deframe()
RA.df$MiSeq_Run<-MiSeq_Run[match(RA.df$Sample.ID, names(MiSeq_Run))]5.5 Boxplot of top 20 RA
pd = position_dodge(width = 0.5)
ggplot(data = RA.df, aes(x=ASV, y=log10_RA, fill=Sample_group))+
stat_boxplot(geom = "errorbar", position = pd, width = 0.25)+
geom_boxplot(width = 0.5, position = pd)+
scale_fill_manual(values = wes_palette(n=3, name = "FantasticFox1"))+
#geom_dotplot(binaxis = 'y', stackdir='center', dotsize = 1)+
#geom_jitter(shape=16, position=position_jitter(0.2))+
#ggtitle("Relative Abundance of the Top 20 Most Abundant ASVs
#in STGG, RNA protect and Environmental Controls")+
ylab("log10(Relative abundance)")+
facet_wrap(~ASV, scales="free")+
theme_bw()+
scale_x_discrete(label=NULL)+
ggsave("Relative_abundances_top20ASVs_Boxplot.pdf", units="in", width=14, height=8, dpi=300)
#theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5), legend.position = "bottom")5.5.1 Top 20 RA table
Grouped_RA.df <- RA.df %>%
mutate(mean_percentage=RA*100)%>%
group_by(ASV, Sample_group) %>%
summarise_each(funs(mean)) %>%
select(ASV, Sample_group, mean_percentage, log10_RA)%>%
arrange(desc(ASV))## Warning: `summarise_each_()` was deprecated in dplyr 0.7.0.
## Please use `across()` instead.## Warning: `funs()` was deprecated in dplyr 0.8.0.
## Please use a list of either functions or lambdas:
##
## # Simple named list:
## list(mean = mean, median = median)
##
## # Auto named with `tibble::lst()`:
## tibble::lst(mean, median)
##
## # Using lambdas
## list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))## Warning in mean.default(Sample.ID): argument is not numeric or logical:
## returning NA
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## returning NAprint(Grouped_RA.df)## # A tibble: 63 x 4
## # Groups: ASV [21]
## ASV Sample_group mean_percentage log10_RA
## <fct> <chr> <dbl> <dbl>
## 1 Tepidimonas_1 blank 10.3 0.0386
## 2 Tepidimonas_1 rna_protect 0 0
## 3 Tepidimonas_1 stgg 46.7 0.165
## 4 Curvibacter_2 blank 13.5 0.0517
## 5 Curvibacter_2 rna_protect 26.5 0.102
## 6 Curvibacter_2 stgg 0 0
## 7 Thermus_3 blank 11.5 0.0454
## 8 Thermus_3 rna_protect 17.3 0.0689
## 9 Thermus_3 stgg 2.00 0.00857
## 10 Schlegelella_4 blank 4.33 0.0180
## # … with 53 more rowsprint(xtable(Grouped_RA.df, type = "latex", digits = 3), include.rownames = FALSE, sanitize.text.function=italic, file = "top20_grouped_RA.tex")5.6 Linear Model of top 20 Most Abundant RAs
5.6.1 Tepidemonas
library(lmerTest)
TepRA <- filter(RA.df, ASV=="Tepidimonas_1")
Tep <- lm(log10_RA ~ Sample_group + MiSeq_Run, data=TepRA)
summary(Tep)##
## Call:
## lm(formula = log10_RA ~ Sample_group + MiSeq_Run, data = TepRA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.07026 -0.02470 0.00000 0.01246 0.13951
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.486e+00 8.574e-01 5.232 3.47e-06 ***
## Sample_grouprna_protect 1.126e-16 1.779e-02 0.000 1
## Sample_groupstgg 8.746e-02 1.352e-02 6.471 4.38e-08 ***
## MiSeq_Run -8.577e-03 1.653e-03 -5.187 4.05e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03794 on 49 degrees of freedom
## Multiple R-squared: 0.7937, Adjusted R-squared: 0.7811
## F-statistic: 62.84 on 3 and 49 DF, p-value: < 2.2e-16par(mfrow=c(2,2)) # Change the panel layout to 2 x 2
plot(Tep)
p1 <- 2.2e-16Tep2 <- glm(log10_RA ~ Sample_group + MiSeq_Run, family = poisson, data = TepRA)## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.080787## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.053875## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.043073## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.052498## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.006935## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.093539## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.028876## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.034398## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.031614## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.089656## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.163423## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.108176## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.216709## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.136475## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.197135## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.158052## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.108242## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.206160## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.178079## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.188005## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.168799## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.139544## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.121951## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.140430## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.117869## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.127468## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.113671## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.135297## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.212650## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.221944## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.204267## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.216070## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.201104par(mfrow=c(2,2))
plot(Tep2)
### Curvibacter
CurvRA <- filter(RA.df, ASV=="Curvibacter_2")
Curv <- lm(log10_RA ~ Sample_group + MiSeq_Run, data=CurvRA)
summary(Curv)##
## Call:
## lm(formula = log10_RA ~ Sample_group + MiSeq_Run, data = CurvRA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.03168 0.00000 0.00000 0.00000 0.01973
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.902e+00 1.941e-01 -30.414 <2e-16 ***
## Sample_grouprna_protect -1.633e-03 4.026e-03 -0.406 0.687
## Sample_groupstgg -1.073e-15 3.060e-03 0.000 1.000
## MiSeq_Run 1.148e-02 3.743e-04 30.682 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.008588 on 49 degrees of freedom
## Multiple R-squared: 0.9733, Adjusted R-squared: 0.9716
## F-statistic: 594.6 on 3 and 49 DF, p-value: < 2.2e-16par(mfrow=c(2,2))
plot(Curv)
p2 <- 2.2e-165.6.2 Thermus_3
Therm3RA <- filter(RA.df, ASV=="Thermus_3")
hist(Therm3RA$log10_RA)
Therm3 <- lm(log10_RA ~ Sample_group + MiSeq_Run, data=Therm3RA)
summary(Therm3)##
## Call:
## lm(formula = log10_RA ~ Sample_group + MiSeq_Run, data = Therm3RA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.029936 -0.006793 -0.001699 0.006432 0.041384
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.4005363 0.2994868 -14.694 <2e-16 ***
## Sample_grouprna_protect -0.0151103 0.0062131 -2.432 0.0187 *
## Sample_groupstgg 0.0017792 0.0047215 0.377 0.7079
## MiSeq_Run 0.0085746 0.0005776 14.846 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01325 on 49 degrees of freedom
## Multiple R-squared: 0.8806, Adjusted R-squared: 0.8733
## F-statistic: 120.5 on 3 and 49 DF, p-value: < 2.2e-16par(mfrow=c(2,2))
plot(Therm3)
### Schlegelella_4
SchlegRA <- filter(RA.df, ASV=="Schlegelella_4")
Schleg <- lm(log10_RA ~ Sample_group + MiSeq_Run, data=SchlegRA)
summary(Schleg)##
## Call:
## lm(formula = log10_RA ~ Sample_group + MiSeq_Run, data = SchlegRA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.02312 0.00000 0.00000 0.00000 0.01351
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.057e+00 1.298e-01 -15.848 < 2e-16 ***
## Sample_grouprna_protect -1.290e-02 2.693e-03 -4.791 1.58e-05 ***
## Sample_groupstgg -3.715e-16 2.047e-03 0.000 1
## MiSeq_Run 4.003e-03 2.504e-04 15.987 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.005745 on 49 degrees of freedom
## Multiple R-squared: 0.8903, Adjusted R-squared: 0.8836
## F-statistic: 132.5 on 3 and 49 DF, p-value: < 2.2e-16par(mfrow=c(2,2))
plot(Schleg)
### Thermus_5
Therm5RA <- filter(RA.df, ASV=="Thermus_5")
Therm5 <- lm(log10_RA ~ Sample_group + MiSeq_Run, data=Therm5RA)
summary(Therm5)##
## Call:
## lm(formula = log10_RA ~ Sample_group + MiSeq_Run, data = Therm5RA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.018648 -0.003348 0.000000 0.002313 0.044376
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.084e+00 2.507e-01 4.323 7.53e-05 ***
## Sample_grouprna_protect 5.630e-17 5.201e-03 0.000 1
## Sample_groupstgg 1.770e-02 3.952e-03 4.479 4.49e-05 ***
## MiSeq_Run -2.072e-03 4.835e-04 -4.286 8.50e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01109 on 49 degrees of freedom
## Multiple R-squared: 0.6867, Adjusted R-squared: 0.6675
## F-statistic: 35.79 on 3 and 49 DF, p-value: 2.127e-12par(mfrow=c(2,2))
plot(Therm5)
### Caulobacter_6
Caul6RA <- filter(RA.df, ASV=="Caulobacter_6")
Caul6 <- lm(log10_RA ~ Sample_group + MiSeq_Run, data=Caul6RA)
summary(Caul6)##
## Call:
## lm(formula = log10_RA ~ Sample_group + MiSeq_Run, data = Caul6RA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0185179 -0.0007313 0.0000000 0.0000000 0.0220961
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.0150806 0.1321547 -7.681 5.89e-10 ***
## Sample_grouprna_protect -0.0021489 0.0027416 -0.784 0.437
## Sample_groupstgg -0.0007313 0.0020835 -0.351 0.727
## MiSeq_Run 0.0019763 0.0002549 7.754 4.55e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.005848 on 49 degrees of freedom
## Multiple R-squared: 0.6956, Adjusted R-squared: 0.6769
## F-statistic: 37.32 on 3 and 49 DF, p-value: 1.056e-12par(mfrow=c(2,2))
plot(Caul6)
### Comamonadaceae_7
Coma7RA <- filter(RA.df, ASV=="Comamonadaceae_7")
Coma7 <- lm(log10_RA ~ Sample_group + MiSeq_Run, data=Coma7RA)
summary(Coma7)##
## Call:
## lm(formula = log10_RA ~ Sample_group + MiSeq_Run, data = Coma7RA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.01220 0.00000 0.00000 0.00000 0.01155
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.056e+00 9.060e-02 -11.661 9.64e-16 ***
## Sample_grouprna_protect -6.302e-03 1.879e-03 -3.353 0.00155 **
## Sample_groupstgg -1.899e-16 1.428e-03 0.000 1.00000
## MiSeq_Run 2.055e-03 1.747e-04 11.763 7.01e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.004009 on 49 degrees of freedom
## Multiple R-squared: 0.8157, Adjusted R-squared: 0.8044
## F-statistic: 72.3 on 3 and 49 DF, p-value: < 2.2e-16par(mfrow=c(2,2))
plot(Coma7)
### Sphingomonas_8
Sphing8RA <- filter(RA.df, ASV=="Sphingomonas_8")
Sphing8 <- lm(log10_RA ~ Sample_group + MiSeq_Run, data=Sphing8RA)
summary(Sphing8)##
## Call:
## lm(formula = log10_RA ~ Sample_group + MiSeq_Run, data = Sphing8RA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.046260 -0.009524 0.000000 0.000000 0.166240
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.688e+00 7.064e-01 3.806 0.000393 ***
## Sample_grouprna_protect -4.504e-16 1.465e-02 0.000 1.000000
## Sample_groupstgg -3.417e-02 1.114e-02 -3.069 0.003500 **
## MiSeq_Run -5.140e-03 1.362e-03 -3.773 0.000435 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03126 on 49 degrees of freedom
## Multiple R-squared: 0.2658, Adjusted R-squared: 0.2208
## F-statistic: 5.912 on 3 and 49 DF, p-value: 0.001587par(mfrow=c(2,2))
plot(Sphing8)
### Bosea_9
Bos9RA <- filter(RA.df, ASV=="Bosea_9")
Bos9 <- lm(log10_RA ~ Sample_group + MiSeq_Run, data=Bos9RA)
summary(Bos9)##
## Call:
## lm(formula = log10_RA ~ Sample_group + MiSeq_Run, data = Bos9RA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.01220 0.00000 0.00000 0.00000 0.01006
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.779e-01 7.201e-02 -12.192 <2e-16 ***
## Sample_grouprna_protect -3.170e-03 1.494e-03 -2.122 0.0389 *
## Sample_groupstgg -1.579e-16 1.135e-03 0.000 1.0000
## MiSeq_Run 1.708e-03 1.389e-04 12.299 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.003186 on 49 degrees of freedom
## Multiple R-squared: 0.8383, Adjusted R-squared: 0.8284
## F-statistic: 84.66 on 3 and 49 DF, p-value: < 2.2e-16par(mfrow=c(2,2))
plot(Bos9)
### Thermus_thermophilus_10
Therm10RA <- filter(RA.df, ASV=="Thermus_thermophilus_10")
Therm10 <- lm(log10_RA ~ Sample_group + MiSeq_Run, data=Therm10RA)
summary(Therm10)##
## Call:
## lm(formula = log10_RA ~ Sample_group + MiSeq_Run, data = Therm10RA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0078176 -0.0016196 0.0000000 0.0006173 0.0107406
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.411e-02 8.154e-02 1.154 0.254
## Sample_grouprna_protect -1.971e-17 1.692e-03 0.000 1.000
## Sample_groupstgg 1.324e-02 1.286e-03 10.302 7.43e-14 ***
## MiSeq_Run -1.800e-04 1.573e-04 -1.144 0.258
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.003608 on 49 degrees of freedom
## Multiple R-squared: 0.7993, Adjusted R-squared: 0.787
## F-statistic: 65.04 on 3 and 49 DF, p-value: < 2.2e-16par(mfrow=c(2,2))
plot(Therm10)
### Sphingobium_11
Sphing11RA <- filter(RA.df, ASV=="Sphingobium_11")
Sphing11 <- lm(log10_RA ~ Sample_group + MiSeq_Run, data=Sphing11RA)
summary(Sphing11)##
## Call:
## lm(formula = log10_RA ~ Sample_group + MiSeq_Run, data = Sphing11RA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0115491 -0.0002148 -0.0002148 0.0000000 0.0157031
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.6595829 0.0883679 -7.464 1.27e-09 ***
## Sample_grouprna_protect -0.0021151 0.0018333 -1.154 0.254
## Sample_groupstgg 0.0002148 0.0013932 0.154 0.878
## MiSeq_Run 0.0012832 0.0001704 7.530 1.01e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.00391 on 49 degrees of freedom
## Multiple R-squared: 0.6579, Adjusted R-squared: 0.6369
## F-statistic: 31.41 on 3 and 49 DF, p-value: 1.799e-11par(mfrow=c(2,2))
plot(Sphing11)
### Pseudomonas_12
Pseudo12RA <- filter(RA.df, ASV=="Pseudomonas_12")
Pseudo12 <- lm(log10_RA ~ Sample_group + MiSeq_Run, data=Pseudo12RA)
summary(Pseudo12)##
## Call:
## lm(formula = log10_RA ~ Sample_group + MiSeq_Run, data = Pseudo12RA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0064744 -0.0036654 -0.0000223 0.0023062 0.0144748
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.1497721 0.1059689 -1.413 0.164
## Sample_grouprna_protect 0.0001223 0.0021984 0.056 0.956
## Sample_groupstgg 0.0023537 0.0016706 1.409 0.165
## MiSeq_Run 0.0002985 0.0002044 1.461 0.150
##
## Residual standard error: 0.004689 on 49 degrees of freedom
## Multiple R-squared: 0.05678, Adjusted R-squared: -0.0009732
## F-statistic: 0.9831 on 3 and 49 DF, p-value: 0.4084par(mfrow=c(2,2))
plot(Pseudo12)
### Methylobacterium_Methylorubrum_13
Meth13RA <- filter(RA.df, ASV=="Methylobacterium_Methylorubrum_13")
Meth13 <- lm(log10_RA ~ Sample_group + MiSeq_Run, data=Meth13RA)
summary(Meth13)##
## Call:
## lm(formula = log10_RA ~ Sample_group + MiSeq_Run, data = Meth13RA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.015940 -0.008144 -0.000422 0.000000 0.066607
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.7862192 0.3388988 2.320 0.0246 *
## Sample_grouprna_protect -0.0004219 0.0070307 -0.060 0.9524
## Sample_groupstgg 0.0019961 0.0053429 0.374 0.7103
## MiSeq_Run -0.0015025 0.0006536 -2.299 0.0258 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.015 on 49 degrees of freedom
## Multiple R-squared: 0.202, Adjusted R-squared: 0.1531
## F-statistic: 4.134 on 3 and 49 DF, p-value: 0.01086par(mfrow=c(2,2))
plot(Meth13)
### Ottowia_14
Otto14RA <- filter(RA.df, ASV=="Ottowia_14")
Otto14 <- lm(log10_RA ~ Sample_group + MiSeq_Run, data=Otto14RA)
summary(Otto14)##
## Call:
## lm(formula = log10_RA ~ Sample_group + MiSeq_Run, data = Otto14RA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.009388 0.000000 0.000000 0.000000 0.012397
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.671e-01 9.035e-02 -5.169 4.32e-06 ***
## Sample_grouprna_protect 1.210e-03 1.874e-03 0.646 0.522
## Sample_groupstgg -8.410e-17 1.424e-03 0.000 1.000
## MiSeq_Run 9.087e-04 1.743e-04 5.215 3.69e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.003998 on 49 degrees of freedom
## Multiple R-squared: 0.5423, Adjusted R-squared: 0.5142
## F-statistic: 19.35 on 3 and 49 DF, p-value: 2.055e-08par(mfrow=c(2,2))
plot(Otto14)
### Azospirillum_15
Azo14RA <- filter(RA.df, ASV=="Azospirillum_15")
Azo14 <- lm(log10_RA ~ Sample_group + MiSeq_Run, data=Azo14RA)
summary(Azo14)##
## Call:
## lm(formula = log10_RA ~ Sample_group + MiSeq_Run, data = Azo14RA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.009572 0.000000 0.000000 0.000000 0.012203
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.467e-01 7.274e-02 -7.515 1.06e-09 ***
## Sample_grouprna_protect -2.832e-03 1.509e-03 -1.876 0.0666 .
## Sample_groupstgg -9.803e-17 1.147e-03 0.000 1.0000
## MiSeq_Run 1.064e-03 1.403e-04 7.581 8.40e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.003219 on 49 degrees of freedom
## Multiple R-squared: 0.6525, Adjusted R-squared: 0.6312
## F-statistic: 30.67 on 3 and 49 DF, p-value: 2.626e-11par(mfrow=c(2,2))
plot(Azo14)
### Meiothermus_silvanus_16
Meio16RA <- filter(RA.df, ASV=="Meiothermus_silvanus_16")
Meio16 <- lm(log10_RA ~ Sample_group + MiSeq_Run, data=Meio16RA)
summary(Meio16)##
## Call:
## lm(formula = log10_RA ~ Sample_group + MiSeq_Run, data = Meio16RA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.00902 0.00000 0.00000 0.00000 0.02444
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.850e-01 9.931e-02 -6.897 9.56e-09 ***
## Sample_grouprna_protect -7.157e-03 2.060e-03 -3.474 0.00108 **
## Sample_groupstgg -1.280e-16 1.566e-03 0.000 1.00000
## MiSeq_Run 1.333e-03 1.915e-04 6.958 7.70e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.004395 on 49 degrees of freedom
## Multiple R-squared: 0.5887, Adjusted R-squared: 0.5635
## F-statistic: 23.37 on 3 and 49 DF, p-value: 1.557e-09par(mfrow=c(2,2))
plot(Meio16)
5.6.3 Staphylococcus_17
Staph17RA <- filter(RA.df, ASV=="Staphylococcus_17")
Staph17 <- lm(log10_RA ~ Sample_group + MiSeq_Run, data=Staph17RA)
summary(Staph17)##
## Call:
## lm(formula = log10_RA ~ Sample_group + MiSeq_Run, data = Staph17RA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.010819 -0.004793 -0.003570 0.003275 0.034368
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3549610 0.2403160 1.477 0.146
## Sample_grouprna_protect 0.0044842 0.0049855 0.899 0.373
## Sample_groupstgg -0.0062834 0.0037887 -1.658 0.104
## MiSeq_Run -0.0006695 0.0004635 -1.445 0.155
##
## Residual standard error: 0.01063 on 49 degrees of freedom
## Multiple R-squared: 0.06854, Adjusted R-squared: 0.01152
## F-statistic: 1.202 on 3 and 49 DF, p-value: 0.3189par(mfrow=c(2,2))
plot(Staph17)
### Nitriliruptoraceae_18
Nitril18RA <- filter(RA.df, ASV=="Nitriliruptoraceae_18")
Nitril18 <- lm(log10_RA ~ Sample_group + MiSeq_Run, data=Nitril18RA)
summary(Nitril18)##
## Call:
## lm(formula = log10_RA ~ Sample_group + MiSeq_Run, data = Nitril18RA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0133870 -0.0026933 -0.0002246 0.0024915 0.0182459
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.6468485 0.1306326 4.952 9.13e-06 ***
## Sample_grouprna_protect -0.0001250 0.0027101 -0.046 0.9634
## Sample_groupstgg -0.0054882 0.0020595 -2.665 0.0104 *
## MiSeq_Run -0.0012324 0.0002519 -4.892 1.12e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.005781 on 49 degrees of freedom
## Multiple R-squared: 0.3784, Adjusted R-squared: 0.3403
## F-statistic: 9.943 on 3 and 49 DF, p-value: 3.14e-05par(mfrow=c(2,2))
plot(Nitril18)
### Paucibacter_19
Pauci19RA <- filter(RA.df, ASV=="Paucibacter_19")
Pauci19 <- lm(log10_RA ~ Sample_group + MiSeq_Run, data=Pauci19RA)
summary(Pauci19)##
## Call:
## lm(formula = log10_RA ~ Sample_group + MiSeq_Run, data = Pauci19RA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.021977 -0.002729 -0.000348 0.003230 0.081139
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.122799 0.325094 3.454 0.00115 **
## Sample_grouprna_protect 0.017391 0.006744 2.579 0.01298 *
## Sample_groupstgg -0.016772 0.005125 -3.272 0.00196 **
## MiSeq_Run -0.002142 0.000627 -3.416 0.00129 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01439 on 49 degrees of freedom
## Multiple R-squared: 0.2676, Adjusted R-squared: 0.2228
## F-statistic: 5.969 on 3 and 49 DF, p-value: 0.001495par(mfrow=c(2,2))
plot(Pauci19)
### Brevundimonas_20
Brev20RA <- filter(RA.df, ASV=="Brevundimonas_20")
Brev20 <- lm(log10_RA ~ Sample_group + MiSeq_Run, data=Brev20RA)
summary(Brev20)##
## Call:
## lm(formula = log10_RA ~ Sample_group + MiSeq_Run, data = Brev20RA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.007648 0.000000 0.000000 0.000000 0.012488
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.461e-01 7.614e-02 -7.172 3.60e-09 ***
## Sample_grouprna_protect -3.999e-03 1.580e-03 -2.532 0.0146 *
## Sample_groupstgg -9.803e-17 1.200e-03 0.000 1.0000
## MiSeq_Run 1.062e-03 1.468e-04 7.235 2.87e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.00337 on 49 degrees of freedom
## Multiple R-squared: 0.6186, Adjusted R-squared: 0.5953
## F-statistic: 26.49 on 3 and 49 DF, p-value: 2.499e-10par(mfrow=c(2,2))
plot(Brev20)
## Combined p-value
P_Value <- c("2.2e-16", "2.2e-16", "2.2e-16", "2.2e-16", "2.127e-12", "1.056e-12", "2.2e-16", "0.001587", "2.2e-16", "2.2e-16", "1.799e-11", "0.4084", "0.01086", "2.055e-08", "2.626e-11", "1.557e-09", "0.3189", "3.14e-05", "0.001495", "2.499e-10")
Adjusted_P_Value <- p.adjust(P_Value, method = "BH")5.7 Table of p-values
top20_names <- rev(as.vector(mean_ab_groups$ASV))
top20_names <- top20_names[! top20_names %in% "Residuals"]
top20_pval <- c(lmp(Tep), lmp(Curv), lmp(Therm3), lmp(Schleg), lmp(Therm5), lmp(Caul6), lmp(Coma7), lmp(Sphing8), lmp(Bos9), lmp(Therm10), lmp(Sphing11), lmp(Pseudo12), lmp(Meth13), lmp(Otto14), lmp(Azo14), lmp(Meio16), lmp(Staph17), lmp(Nitril18), lmp(Pauci19), lmp(Brev20))
top20_adjpval <- p.adjust(top20_pval, method = "BH")
top20_R2 <- c(summary(Tep)$r.squared, summary(Curv)$r.squared, summary(Therm3)$r.squared, summary(Schleg)$r.squared, summary(Therm5)$r.squared, summary(Caul6)$r.squared, summary(Coma7)$r.squared, summary(Sphing8)$r.squared, summary(Bos9)$r.squared, summary(Therm10)$r.squared, summary(Sphing11)$r.squared, summary(Pseudo12)$r.squared, summary(Meth13)$r.squared, summary(Otto14)$r.squared, summary(Azo14)$r.squared, summary(Meio16)$r.squared, summary(Staph17)$r.squared, summary(Nitril18)$r.squared, summary(Pauci19)$r.squared, summary(Brev20)$r.squared)
top20_pval.df <- data.frame(top20_names, top20_R2, top20_pval, top20_adjpval)
#top20_pval.df <- data.frame(top20_names, P_Value, Adjusted_P_Value)
top20_pval.df <- top20_pval.df %>%
rename(ASV = top20_names)
print(top20_pval.df)## ASV top20_R2 top20_pval top20_adjpval
## 1 Brevundimonas_20 0.79371378 8.131030e-17 2.323151e-16
## 2 Azospirillum_15 0.97326473 1.627495e-38 3.254990e-37
## 3 Ottowia_14 0.88063171 1.290990e-22 8.606602e-22
## 4 Meiothermus_silvanus_16 0.89027117 1.649154e-23 1.649154e-22
## 5 Sphingobium_11 0.68667011 2.127411e-12 4.727579e-12
## 6 Bosea_9 0.69558205 1.055536e-12 2.638840e-12
## 7 Pseudomonas_12 0.81571522 5.197198e-18 2.078879e-17
## 8 Thermus_thermophilus_10 0.26576411 1.587473e-03 1.867616e-03
## 9 Comamonadaceae_7 0.83827929 2.146263e-19 1.073132e-18
## 10 Staphylococcus_17 0.79927301 4.177428e-17 1.392476e-16
## 11 Caulobacter_6 0.65786356 1.798508e-11 3.597016e-11
## 12 Nitriliruptoraceae_18 0.05677527 4.084309e-01 4.084309e-01
## 13 Methylobacterium_Methylorubrum_13 0.20198160 1.086480e-02 1.207200e-02
## 14 Paucibacter_19 0.54226699 2.054586e-08 2.935122e-08
## 15 Schlegelella_4 0.65248336 2.625739e-11 4.774071e-11
## 16 Sphingomonas_8 0.58866225 1.556789e-09 2.395060e-09
## 17 Thermus_5 0.06854388 3.189125e-01 3.356973e-01
## 18 Thermus_3 0.37840492 3.139720e-05 4.186293e-05
## 19 Curvibacter_2 0.26764887 1.495191e-03 1.867616e-03
## 20 Tepidimonas_1 0.61861982 2.498861e-10 4.164768e-10write.table(top20_pval.df,file="Top_20_Pval.txt")
italic <- function(x){
paste("\textit{",
x,
"}")
}
print(xtable(top20_pval.df, digits = c( 0, 0, 3, -3, -3), type = "latex"), sanitize.text.function=italic, include.rownames=FALSE, file = "Top20_pval.tex")mean_ab_groups_str<-stack.dfT%>%
group_by(ASV, Sample_group)%>%
summarise(mean=mean(RA))%>%
mutate(mean_percentage=mean*100, Group=ifelse(Sample_group=="blank","STGG","rna_protect"))
##Plot Stack Bargraph
ggplot(mean_ab_groups_str, aes(x = Sample_group, y = mean_percentage, fill = ASV)) +
geom_bar(stat = "identity", position = "stack") +
ylab("Relative abundance(%)")+
xlab("Sample")+
scale_fill_manual(values = cols)+
#facet_grid(.~Sample_group, scales = "free")+
theme_bw() +
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5)) +
ggsave("Relative_abundances_top20OTUs_Sample_type2.tiff", units="in", width=8, height=5, dpi=300, compression = 'lzw')
# Calculate the Top 20 most abundant taxa in each sample type ##STGG
ps.top20.STGG <- prune_samples(sample_data(ps_filtered)$Sample_type=="stgg", ps_filtered)
metadata_STGG <- data.frame(sample_data(ps.top20.STGG))
OTU2<- as(otu_table(ps.top20.STGG), "matrix")
if(taxa_are_rows(ps.top20.STGG)){OTU2 <- t(OTU2)}
OTU2.t <-t(OTU2)
otu_table_ord2<-OTU2.t[order(rowSums(OTU2.t),decreasing=T),]
n = 20 # only show top 20 OTUs
otu_table_ord_RA2 <- t(t(otu_table_ord2)/colSums(otu_table_ord2))
stack.dfT_STGG <- cbind(data.frame(Sample.ID=rownames(metadata_STGG)), t(otu_table_ord_RA2[1:n, ]) %>%
data.frame(check.names = F) %>%
mutate(Residuals = 1 - rowSums(.))) %>%
gather(key=ASV, value=RA, -Sample.ID) %>%
mutate(ASV = factor(ASV, levels = rev(unique(ASV))))## Warning: attributes are not identical across measure variables;
## they will be droppedmetadata_STGG$Sample <-rownames(metadata_STGG)
Sample_group2<-metadata_STGG%>%
select(Sample, Sample_type) %>%
deframe()
stack.dfT_STGG$Sample_group<- Sample_group2
mean_ab_groups_STGG<-stack.dfT_STGG%>%
group_by(ASV)%>%
summarise(mean=mean(RA))%>%
mutate(mean_percentage=mean*100)
mean_ab_groups_str_STGG<-stack.dfT_STGG%>%
group_by(ASV, Sample_group)%>%
summarise(mean=mean(RA))%>%
mutate(mean_percentage=mean*100)
##Plot Stack Bargraph
ggplot(mean_ab_groups_str_STGG, aes(x = Sample_group, y = mean_percentage, fill = ASV)) +
geom_bar(stat = "identity", position = "stack") +
ylab("Relative abundance(%)")+
xlab("STGG")+
scale_fill_manual(values = cols)+
#facet_grid(.~Sample_group, scales = "free")+
theme_bw() +
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))+
scale_x_discrete(label=NULL)+
ggsave("Relative_abundances_top20OTUs_STGG.tiff", units="in", width=8, height=5, dpi=300, compression = 'lzw')
5.8 Blank 514
ps.top20.b514 <- prune_samples(sample_data(ps_filtered)$Sample_type=="blank", ps_filtered)
ps.top20.b514 <- prune_samples(sample_data(ps.top20.b514)$Run_No=="514", ps.top20.b514)
metadata_b514 <- data.frame(sample_data(ps.top20.b514))
OTU2<- as(otu_table(ps.top20.b514), "matrix")
if(taxa_are_rows(ps.top20.b514)){OTU2 <- t(OTU2)}
OTU2.t <-t(OTU2)
otu_table_ord2<-OTU2.t[order(rowSums(OTU2.t),decreasing=T),]
n = 20 # only show top 20 OTUs
otu_table_ord_RA2 <- t(t(otu_table_ord2)/colSums(otu_table_ord2))
stack.dfT_b514 <- cbind(data.frame(Sample.ID=rownames(metadata_b514)), t(otu_table_ord_RA2[1:n, ]) %>%
data.frame(check.names = F) %>%
mutate(Residuals = 1 - rowSums(.))) %>%
gather(key=ASV, value=RA, -Sample.ID) %>%
mutate(ASV = factor(ASV, levels = rev(unique(ASV))))## Warning: attributes are not identical across measure variables;
## they will be droppedmetadata_b514$Sample <-rownames(metadata_b514)
Sample_group2<-metadata_b514%>%
select(Sample, Sample_type) %>%
deframe()
stack.dfT_b514$Sample_group<- Sample_group2
mean_ab_groups_b514<-stack.dfT_b514%>%
group_by(ASV)%>%
summarise(mean=mean(RA))%>%
mutate(mean_percentage=mean*100)
mean_ab_groups_str_b514<-stack.dfT_b514%>%
group_by(ASV, Sample_group)%>%
summarise(mean=mean(RA))%>%
mutate(mean_percentage=mean*100)
##Plot Stack Bargraph
ggplot(mean_ab_groups_str_b514, aes(x = Sample_group, y = mean_percentage, fill = ASV)) +
geom_bar(stat = "identity", position = "stack") +
ylab("Relative abundance(%)")+
xlab("STGG")+
scale_fill_manual(values = cols)+
#facet_grid(.~Sample_group, scales = "free")+
theme_bw() +
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))+
scale_x_discrete(label=NULL)#+
#ggsave("Relative_abundances_top20OTUs_b514.tiff", units="in", width=8, height=5, dpi=300, compression = 'lzw')5.9 Run514
ps.top20.514 <- prune_samples(sample_data(ps_filtered)$Run_No=="514", ps_filtered)
OTU3<- as(otu_table(ps.top20.514), "matrix")
if(taxa_are_rows(ps.top20.514)){OTU3 <- t(OTU3)}
OTU3.t <-t(OTU3)
otu_table_ord3<-OTU3.t[order(rowSums(OTU3.t),decreasing=T),]
n = 20 # only show top 20 OTUs
otu_table_ord_RA3 <- t(t(otu_table_ord3)/colSums(otu_table_ord3))
stack.dfT_514 <- cbind(data.frame(Sample.ID=rownames(metadata514)), t(otu_table_ord_RA3[1:n, ]) %>%
data.frame(check.names = F) %>%
mutate(Residuals = 1 - rowSums(.))) %>%
gather(key=ASV, value=RA, -Sample.ID) %>%
mutate(ASV = factor(ASV, levels = rev(unique(ASV))))## Warning: attributes are not identical across measure variables;
## they will be droppedmetadata514$Sample <-rownames(metadata514)
Sample_group3<-metadata514%>%
select(Sample, Sample_type) %>%
deframe()
stack.dfT_514$Sample_group<- Sample_group3
mean_ab_groups_str_514<-stack.dfT_514%>%
group_by(ASV, Sample_group)%>%
summarise(mean=mean(RA))%>%
mutate(mean_percentage=mean*100)%>%
arrange(mean_percentage)
##Plot Stack Bargraph
ggplot(mean_ab_groups_str_514, aes(x = Sample_group, y = mean_percentage, fill = ASV)) +
geom_bar(stat = "identity", position = "stack") +
ylab("Relative abundance(%)")+
xlab("Sample Type")+
scale_fill_manual(values = cols)+
#facet_grid(.~Sample_group, scales = "free")+
theme_bw() +
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))+
ggsave("Relative_abundances_top20ASVs_Run_514.tiff", units="in", width=8, height=5, dpi=300, compression = 'lzw')
### Were any taxa exclusively found in STGG?
# Exclusive to STGG
STGG_truetaxa.df <- full_join(mean_ab_groups_str_STGG, mean_ab_groups_str_b514, "ASV", suffix = c("_STGG", "_Blank")) %>%
mutate(Exclusive = is.na(Sample_group_Blank)) %>%
select(ASV, mean_percentage_STGG, mean_percentage_Blank, Exclusive) %>%
arrange(desc(mean_percentage_STGG))
print(STGG_truetaxa.df)## # A tibble: 28 x 4
## # Groups: ASV [28]
## ASV mean_percentage_ST… mean_percentage_Bl… Exclusive
## <fct> <dbl> <dbl> <lgl>
## 1 Tepidimonas_1 46.7 20.5 FALSE
## 2 Residuals 15.7 24.1 FALSE
## 3 Thermus_5 8.76 4.48 FALSE
## 4 Methylobacterium_Methyloru… 3.80 3.41 FALSE
## 5 Thermus_thermophilus_10 3.49 NA TRUE
## 6 Sphingomonas_8 2.89 12.3 FALSE
## 7 Thermus_3 2.00 1.63 FALSE
## 8 Methylobacterium_Methyloru… 1.84 1.34 FALSE
## 9 Nitriliruptoraceae_18 1.84 3.15 FALSE
## 10 Methylobacterium_Methyloru… 1.73 NA TRUE
## # … with 18 more rowsprint(which(STGG_truetaxa.df$Exclusive))## [1] 5 10 11 14 18 19 215.10 RNA
ps.top20.RNA <- prune_samples(sample_data(ps_filtered)$Sample_type=="rna_protect", ps_filtered)
metadata_RNA <- data.frame(sample_data(ps.top20.RNA))
OTU4<- as(otu_table(ps.top20.RNA), "matrix")
if(taxa_are_rows(ps.top20.RNA)){OTU4 <- t(OTU4)}
OTU4.t <-t(OTU4)
otu_table_ord4<-OTU4.t[order(rowSums(OTU4.t),decreasing=T),]
n = 20 # only show top 20 OTUs
otu_table_ord_RA4 <- t(t(otu_table_ord4)/colSums(otu_table_ord4))
stack.dfT_RNA <- cbind(data.frame(Sample.ID=rownames(metadata_RNA)), t(otu_table_ord_RA4[1:n, ]) %>%
data.frame(check.names = F) %>%
mutate(Residuals = 1 - rowSums(.))) %>%
gather(key=ASV, value=RA, -Sample.ID) %>%
mutate(ASV = factor(ASV, levels = rev(unique(ASV))))## Warning: attributes are not identical across measure variables;
## they will be droppedmetadata_RNA$Sample <-rownames(metadata_RNA)
Sample_group4<-metadata_RNA%>%
select(Sample, Sample_type) %>%
deframe()
stack.dfT_RNA$Sample_group<- Sample_group4
mean_ab_groups_str_RNA<-stack.dfT_RNA%>%
group_by(ASV, Sample_group)%>%
summarise(mean=mean(RA))%>%
mutate(mean_percentage=mean*100)
##Plot Stack Bargraph
ggplot(mean_ab_groups_str_RNA, aes(x = Sample_group, y = mean_percentage, fill = ASV)) +
geom_bar(stat = "identity", position = "stack") +
ylab("Relative abundance(%)")+
xlab("RNA Protect")+
scale_fill_manual(values = cols)+
#facet_grid(.~Sample_group, scales = "free")+
theme_bw() +
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))+
scale_x_discrete(label=NULL)+
ggsave("Relative_abundances_top20ASVs_RNA_Protect.tiff", units="in", width=8, height=5, dpi=300, compression = 'lzw')
## Blank 523
ps.top20.b523 <- prune_samples(sample_data(ps_filtered)$Sample_type=="blank", ps_filtered)
ps.top20.b523 <- prune_samples(sample_data(ps.top20.b523)$Run_No=="523", ps.top20.b523)
metadata_b523 <- data.frame(sample_data(ps.top20.b523))
OTU2<- as(otu_table(ps.top20.b523), "matrix")
if(taxa_are_rows(ps.top20.b523)){OTU2 <- t(OTU2)}
OTU2.t <-t(OTU2)
otu_table_ord2<-OTU2.t[order(rowSums(OTU2.t),decreasing=T),]
n = 20 # only show top 20 OTUs
otu_table_ord_RA2 <- t(t(otu_table_ord2)/colSums(otu_table_ord2))
stack.dfT_b523 <- cbind(data.frame(Sample.ID=rownames(metadata_b523)), t(otu_table_ord_RA2[1:n, ]) %>%
data.frame(check.names = F) %>%
mutate(Residuals = 1 - rowSums(.))) %>%
gather(key=ASV, value=RA, -Sample.ID) %>%
mutate(ASV = factor(ASV, levels = rev(unique(ASV))))## Warning: attributes are not identical across measure variables;
## they will be droppedmetadata_b523$Sample <-rownames(metadata_b523)
Sample_group2<-metadata_b523%>%
select(Sample, Sample_type) %>%
deframe()
stack.dfT_b523$Sample_group<- Sample_group2
mean_ab_groups_b523<-stack.dfT_b523%>%
group_by(ASV)%>%
summarise(mean=mean(RA))%>%
mutate(mean_percentage=mean*100)
mean_ab_groups_str_b523<-stack.dfT_b523%>%
group_by(ASV, Sample_group)%>%
summarise(mean=mean(RA))%>%
mutate(mean_percentage=mean*100)
##Plot Stack Bargraph
ggplot(mean_ab_groups_str_b523, aes(x = Sample_group, y = mean_percentage, fill = ASV)) +
geom_bar(stat = "identity", position = "stack") +
ylab("Relative abundance(%)")+
xlab("STGG")+
scale_fill_manual(values = cols)+
#facet_grid(.~Sample_group, scales = "free")+
theme_bw() +
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))+
scale_x_discrete(label=NULL)#+
#ggsave("Relative_abundances_top20OTUs_b523.tiff", units="in", width=8, height=5, dpi=300, compression = 'lzw')5.11 Run 523
ps.top20.523 <- prune_samples(sample_data(ps_filtered)$Run_No=="523", ps_filtered)
OTU5<- as(otu_table(ps.top20.523), "matrix")
if(taxa_are_rows(ps.top20.523)){OTU5 <- t(OTU5)}
OTU5.t <-t(OTU5)
otu_table_ord5<-OTU5.t[order(rowSums(OTU5.t),decreasing=T),]
n = 20 # only show top 20 OTUs
otu_table_ord_RA5 <- t(t(otu_table_ord5)/colSums(otu_table_ord5))
stack.dfT_523 <- cbind(data.frame(Sample.ID=rownames(metadata523)), t(otu_table_ord_RA5[1:n, ]) %>%
data.frame(check.names = F) %>%
mutate(Residuals = 1 - rowSums(.))) %>%
gather(key=ASV, value=RA, -Sample.ID) %>%
mutate(ASV = factor(ASV, levels = rev(unique(ASV))))## Warning: attributes are not identical across measure variables;
## they will be droppedmetadata523$Sample <-rownames(metadata523)
Sample_group5<-metadata523%>%
select(Sample, Sample_type) %>%
deframe()
stack.dfT_523$Sample_group<- Sample_group5
mean_ab_groups_str_523<-stack.dfT_523%>%
group_by(ASV, Sample_group)%>%
summarise(mean=mean(RA))%>%
mutate(mean_percentage=mean*100)
##Plot Stack Bargraph
ggplot(mean_ab_groups_str_523, aes(x = Sample_group, y = mean_percentage, fill = ASV)) +
geom_bar(stat = "identity", position = "stack") +
ylab("Relative abundance(%)")+
xlab("Sample Type")+
scale_fill_manual(values = cols)+
#facet_grid(.~Sample_group, scales = "free")+
theme_bw() +
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))+
ggsave("Relative_abundances_top20ASVs_Run_523.tiff", units="in", width=8, height=5, dpi=300, compression = 'lzw')
5.11.1 Were any taxa exclusively found in RNA Protect?
# Exclusive to STGG
RNA_truetaxa.df <- full_join(mean_ab_groups_str_RNA, mean_ab_groups_str_b523, "ASV", suffix = c("_RNA", "_Blank")) %>%
mutate(Exclusive = is.na(Sample_group_Blank)) %>%
select(ASV, mean_percentage_RNA, mean_percentage_Blank, Exclusive) %>%
arrange(desc(mean_percentage_RNA))
print(RNA_truetaxa.df)## # A tibble: 23 x 4
## # Groups: ASV [23]
## ASV mean_percentage_RNA mean_percentage_Blank Exclusive
## <fct> <dbl> <dbl> <lgl>
## 1 Curvibacter_2 26.5 26.9 FALSE
## 2 Residuals 19.0 10.9 FALSE
## 3 Thermus_3 17.3 21.4 FALSE
## 4 Schlegelella_4 5.50 8.67 FALSE
## 5 Paucibacter_19 5.07 0.625 FALSE
## 6 Caulobacter_6 3.88 4.37 FALSE
## 7 Comamonadaceae_7 2.86 4.36 FALSE
## 8 Bosea_9 2.86 3.61 FALSE
## 9 Sphingobium_11 2.21 2.70 FALSE
## 10 Ottowia_14 2.20 1.91 FALSE
## # … with 13 more rowsprint(which(RNA_truetaxa.df$Exclusive))## [1] 12 216 Biomass
6.1 X16s_conc_pg_ul
6.1.1 Does Biomass Vary with Miseq Run
ggplot(metadata_filtered, aes(as.factor(Run_No), X16s_conc_pg_ul, colour=as.factor(Run_No)))+
geom_boxplot()+
scale_colour_manual(values = wes_palette(n=3, name = "FantasticFox1"))+
geom_jitter(shape=16, position=position_jitter(0.2))
ggplot(metadata_filtered, aes(x = Sample, y=X16s_conc_pg_ul, fill = Sample_type))+
geom_bar(stat = "identity")+
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5), legend.position = "bottom")+
labs(x = "Sample", y = bquote('16s rRNA Concentration ('*~pg~ mu~l^-1*')'))
biomass.data <- metadata_filtered %>%
select(Sample_type, Run_No, X16s_conc_pg_ul, PicoGreen)
hist(biomass.data$X16s_conc_pg_ul)
biomass.data <- biomass.data %>%
mutate(log10_X16s=log10(X16s_conc_pg_ul+1))%>%
mutate(sqrt_X16s=sqrt(X16s_conc_pg_ul))
hist(biomass.data$log10_X16s)
hist(biomass.data$sqrt_X16s)
Bm1 <- wilcox.test(biomass.data$X16s_conc_pg_ul ~ biomass.data$Run_No)## Warning in wilcox.test.default(x = c(0.01, 0.01, 0.01, 0.01, 0.01, 0.02, :
## cannot compute exact p-value with tiesBm1##
## Wilcoxon rank sum test with continuity correction
##
## data: biomass.data$X16s_conc_pg_ul by biomass.data$Run_No
## W = 384, p-value = 0.3244
## alternative hypothesis: true location shift is not equal to 06.1.2 Does Biomass vary with Sample Type
ggplot(biomass.data, aes(Sample_type, X16s_conc_pg_ul, fill=Sample_type))+
stat_boxplot(geom = "errorbar", width = 0.25)+
geom_boxplot( ) +
scale_fill_manual(values = wes_palette(n=3, name = "FantasticFox1"))+
geom_jitter(shape=16, position=position_jitter(0.2))+
labs(x="Sample Type", y=bquote('16s rRNA Concentration ('*~pg~ mu~l^-1*')'))+
#ggtitle("Biomass of STGG, RNA Protect and Environmental Blank Samples") +
theme_bw()+
ggsave("Biomass_X16s_Sample_type_BP.pdf", units="in", width=4, height=3, dpi=300)
Bm2 <- aov(X16s_conc_pg_ul ~ Sample_type + Run_No, data = biomass.data)
plot(Bm2)
# Log Transform
Bm2.1 <- aov(log10_X16s ~ Sample_type + Run_No, data = biomass.data)
plot(Bm2.1)
summary(Bm2.1)## Df Sum Sq Mean Sq F value Pr(>F)
## Sample_type 2 0.007584 0.003792 16.90 2.62e-06 ***
## Run_No 1 0.001640 0.001640 7.31 0.0094 **
## Residuals 49 0.010995 0.000224
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Non-parametric as no model fits a normal distribution
Bm2.3 <- kruskal.test(X16s_conc_pg_ul ~ Sample_type, data = biomass.data)
Bm2.3 ##
## Kruskal-Wallis rank sum test
##
## data: X16s_conc_pg_ul by Sample_type
## Kruskal-Wallis chi-squared = 25.459, df = 2, p-value = 2.962e-06# Perform Dunn post Hoc test to identify difference between groups
Bm2.3.DT <- dunnTest(X16s_conc_pg_ul ~ Sample_type, data = biomass.data, method = "bh")## Warning: Sample_type was coerced to a factor.Bm2.3.DT## Comparison Z P.unadj P.adj
## 1 blank - rna_protect 0.4176724 6.761866e-01 6.761866e-01
## 2 blank - stgg -4.6640505 3.100453e-06 9.301359e-06
## 3 rna_protect - stgg -3.5637888 3.655401e-04 5.483101e-04Data could not be transformed to meet assumptions so non-parametric tests were used.
6.1.3 In run 514?
biomass.data_514 <- filter(biomass.data, Run_No==514)
hist(biomass.data_514$X16s_conc_pg_ul)
hist(biomass.data_514)
ggplot(metadata514, aes(Sample_type, X16s_conc_pg_ul, colour=Sample_type))+
geom_boxplot()+
scale_colour_manual(values = wes_palette(n=3, name = "FantasticFox1"))+
geom_jitter(shape=16, position=position_jitter(0.2))
Bm3 <- aov(X16s_conc_pg_ul ~ Sample_type, data = metadata514)
Bm3## Call:
## aov(formula = X16s_conc_pg_ul ~ Sample_type, data = metadata514)
##
## Terms:
## Sample_type Residuals
## Sum of Squares 0.04817120 0.03792577
## Deg. of Freedom 1 31
##
## Residual standard error: 0.03497731
## Estimated effects may be unbalanced6.1.4 In run 523?
ggplot(metadata523, aes(Sample_type, X16s_conc_pg_ul, colour=Sample_type))+
geom_boxplot()+
scale_colour_manual(values = wes_palette(n=3, name = "FantasticFox1"))+
geom_jitter(shape=16, position=position_jitter(0.2))
Bm4 <- aov(X16s_conc_pg_ul ~ Sample_type, data = metadata523)
summary(Bm4)## Df Sum Sq Mean Sq F value Pr(>F)
## Sample_type 1 0.00314 0.003138 1.685 0.211
## Residuals 18 0.03351 0.0018626.2 PicoGreen
6.2.1 Does BM vary with MiSeq Run?
ggplot(metadata_filtered, aes(as.factor(Run_No), PicoGreen, colour=as.factor(Run_No)))+
geom_boxplot()+
scale_colour_manual(values = wes_palette(n=3, name = "FantasticFox1"))+
geom_jitter(shape=16, position=position_jitter(0.2))
ggplot(metadata_filtered, aes(x = Sample, y=PicoGreen, fill = Sample_type))+
geom_bar(stat = "identity")+
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5), legend.position = "bottom")
Bm5 <- aov(PicoGreen ~ Run_No, data = metadata_filtered)
summary(Bm5)## Df Sum Sq Mean Sq F value Pr(>F)
## Run_No 1 18.3 18.27 1.643 0.206
## Residuals 51 567.1 11.126.2.2 Does BM vary with Sample Type?
ggplot(metadata_filtered, aes(Sample_type, PicoGreen, colour=Sample_type))+
geom_boxplot( ) +
scale_colour_manual(values = wes_palette(n=3, name = "FantasticFox1"))+
geom_jitter(shape=16, position=position_jitter(0.2))
Bm6 <- aov(PicoGreen ~ Sample_type, data = metadata_filtered)
summary(Bm6)## Df Sum Sq Mean Sq F value Pr(>F)
## Sample_type 2 106.2 53.12 5.543 0.00669 **
## Residuals 50 479.1 9.58
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Perform tukey HSD post Hoc test to identify difference between groups
Bm6.tukey <- TukeyHSD(Bm6, conf.level = 0.95)
Bm6.tukey## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = PicoGreen ~ Sample_type, data = metadata_filtered)
##
## $Sample_type
## diff lwr upr p adj
## rna_protect-blank 4.336962 1.1530735 7.5208495 0.0051412
## stgg-blank 1.368462 -0.8554307 3.5923538 0.3060525
## stgg-rna_protect -2.968500 -6.2521312 0.3151312 0.08388186.2.3 In run 514?
ggplot(metadata514, aes(Sample_type, PicoGreen, colour=Sample_type))+
geom_boxplot()+
scale_colour_manual(values = wes_palette(n=3, name = "FantasticFox1"))+
geom_jitter(shape=16, position=position_jitter(0.2))
Bm7 <- aov(PicoGreen ~ Sample_type, data = metadata514)
summary(Bm7)## Df Sum Sq Mean Sq F value Pr(>F)
## Sample_type 1 27.5 27.52 2.344 0.136
## Residuals 31 364.0 11.746.2.4 In run 523?
ggplot(metadata523, aes(Sample_type, PicoGreen, colour=Sample_type))+
geom_boxplot()+
scale_colour_manual(values = wes_palette(n=3, name = "FantasticFox1"))+
geom_jitter(shape=16, position=position_jitter(0.2))
Bm8 <- aov(PicoGreen ~ Sample_type, data = metadata523)
summary(Bm8)## Df Sum Sq Mean Sq F value Pr(>F)
## Sample_type 1 66.96 66.96 11.09 0.00372 **
## Residuals 18 108.64 6.04
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 16.2.5 Is there a correlation between X16s_conc_pg_ul and PicoGreen?
ggplot(data = metadata_filtered, aes(x=X16s_conc_pg_ul, y=PicoGreen))+
geom_point()
ggscatter(metadata_filtered, x = "X16s_conc_pg_ul", y = "PicoGreen", add = "reg.line", conf.int = TRUE, cor.coef = TRUE, cor.method = "pearson",)
ggscatter(metadata_filtered, x = "PicoGreen", y = "X16s_conc_pg_ul", add = "reg.line", conf.int = TRUE, cor.coef = TRUE, cor.method = "pearson",)
# Decontamination ## Inspect library Sizes Plot library sizes (number of reads) in each sample as a function of whether that sample was a true positive sample (STGG or RNA Protect) or negative control. As two sample types have been analysed decontam should be done separately for each sample type. ### STGG and RNA/Blanks as negative controls
#inspect library sizes
library.df <- as.data.frame((sample_data(ps_filtered)))
library.df$LibrarySize <- sample_sums(ps_filtered)
library.df <- library.df[order(library.df$LibrarySize),]
library.df$Index <- seq(nrow(library.df))
ggplot(data=library.df, aes(x=Index, y=LibrarySize, colour=Sample_type)) +
geom_point()
#Create phlyoseq object containing only STGG and control samples
psSTGG <- subset_samples(ps_filtered, sample_data(ps_filtered)$Sample_type!="rna_protect")
#inspect library sizes
STGG.df <- as.data.frame((sample_data(psSTGG)))
STGG.df$LibrarySize <- sample_sums(psSTGG)
STGG.df <- STGG.df[order(STGG.df$LibrarySize),]
STGG.df$Index <- seq(nrow(STGG.df))
ggplot(data=STGG.df, aes(x=Index, y=LibrarySize, colour=Sample_type)) +
geom_point()
Whilst the majority of STGG samples fall between 1250 and 5000 reads, blanks were found across all sample sizes accounting for the lowest (~0 reads) and the highest (10,000 reads). No obvious trend or distinction between true samples and controls was observed.
6.3 Frequency
contamdf.freq <- isContaminant(psSTGG, method = "frequency", conc = "X16s_conc_pg_ul", batch = "Run_No")
head(contamdf.freq)## freq prev p.freq p.prev p contaminant
## Tepidimonas_1 0.26088543 33 0.9999124 NA 0.9999124 FALSE
## Curvibacter_2 0.07605064 13 0.9999993 NA 0.9999993 FALSE
## Thermus_3 0.07383025 36 0.8566482 NA 0.8566482 FALSE
## Schlegelella_4 0.02448996 13 0.9999182 NA 0.9999182 FALSE
## Thermus_5 0.05072835 30 0.9999725 NA 0.9999725 FALSE
## Caulobacter_6 0.01283392 13 0.9982599 NA 0.9982599 FALSEtable(contamdf.freq$contaminant)##
## FALSE TRUE
## 231 18head(which(contamdf.freq$contaminant))## [1] 19 21 24 36 53 55The frequency method has identified 13 ASVs as potential non-stgg contaminants. Compare non contaminant ASV (1) with contaminant ASV (19).
set.seed(10)
plot_frequency(psSTGG, taxa_names(psSTGG)[c(19,1)], conc = "PicoGreen") +
xlab("DNA Concentration (")## Warning: Transformation introduced infinite values in continuous y-axis
set.seed(67)
plot_frequency(psSTGG, taxa_names(psSTGG)[sample(which(contamdf.freq$contaminant),2)], conc="PicoGreen") +
xlab("DNA Concentration (PicoGreen fluorescent intensity)")## Warning: Transformation introduced infinite values in continuous y-axis
6.4 Prevalence
sample_data(psSTGG)$is.neg <- sample_data(psSTGG)$Sample_type == "blank"
contamdf.prev <- isContaminant(psSTGG, method="prevalence", neg="is.neg", threshold = 0.1)
table(contamdf.prev$contaminant)##
## FALSE TRUE
## 194 55head(which(contamdf.prev$contaminant))## [1] 2 4 6 7 9 116.4.1 Inspect the number of times these taxa were observed in positive and negative samples
# Make phyloseq object of presence-absence in negative controls and true samples
ps.pa.STGG <- transform_sample_counts(psSTGG, function(abund) 1*(abund>0))
ps.pa.neg.STGG <- prune_samples(sample_data(ps.pa.STGG)$Sample_type == "blank", ps.pa.STGG)
ps.pa.pos.STGG <- prune_samples(sample_data(ps.pa.STGG)$Sample_type == "stgg", ps.pa.STGG)
# Make data.frame of prevalence in positive and negative samples
df.pa.STGG <- data.frame(pa.pos.STGG=taxa_sums(ps.pa.pos.STGG), pa.neg.STGG=taxa_sums(ps.pa.neg.STGG),
contaminant=contamdf.prev$contaminant)
ggplot(data=df.pa.STGG, aes(x=pa.neg.STGG, y=pa.pos.STGG, color=contaminant)) + geom_point() +
xlab("Prevalence (Enivronental Controls)") + ylab("Prevalence (STGG Samples)")
## Decontam with both sample types
#Add column to sample data that assgins logic to control samples
sample_data(ps_filtered)$is.neg <- sample_data(ps_filtered)$Sample_type == "blank"
#Identify true (STGG/RNA Protect)
contamdf.prev.all <- isContaminant(ps_filtered, neg ="is.neg", method = "prevalence", threshold = 0.1, batch = sample_data(ps_filtered)$Run_No)
head(which(contamdf.prev.all$contaminant))## [1] 29 36 39 44 46 496.5 isNnotContam
As samples are of low biomass and a large proportion of sequences are likely to be contaminants isNotContaminant {decontam} was used to identify true (STGG) reads from reads derived from environmental contamination.
#Add column to sample data that assgins logic to control samples
sample_data(ps_filtered)$is.neg <- sample_data(ps_filtered)$Sample_type != "stgg"
#Identify true (STGG)
notcontamdf <- isNotContaminant(ps_filtered, neg ="is.neg", threshold = 0.5, detailed = TRUE)
table(notcontamdf$not.contaminant)##
## FALSE TRUE
## 91 158head(which(notcontamdf$not.contaminant))## [1] 1 3 5 8 10 12print(notcontamdf)## freq prev p.freq
## Tepidimonas_1 2.264289e-01 33 NA
## Curvibacter_2 1.009841e-01 20 NA
## Thermus_3 8.688962e-02 43 NA
## Schlegelella_4 2.852376e-02 19 NA
## Thermus_5 4.402838e-02 30 NA
## Caulobacter_6 1.625848e-02 19 NA
## Comamonadaceae_7 1.447510e-02 19 NA
## Sphingomonas_8 4.107705e-02 28 NA
## Bosea_9 1.262668e-02 19 NA
## Thermus_thermophilus_10 1.408733e-02 23 NA
## Sphingobium_11 9.735880e-03 20 NA
## Pseudomonas_12 1.299774e-02 41 NA
## Methylobacterium_Methylorubrum_13 2.293551e-02 28 NA
## Ottowia_14 7.588832e-03 15 NA
## Azospirillum_15 7.557659e-03 18 NA
## Meiothermus_silvanus_16 8.396799e-03 19 NA
## Staphylococcus_17 1.600792e-02 33 NA
## Nitriliruptoraceae_18 1.665024e-02 47 NA
## Paucibacter_19 2.561865e-02 48 NA
## Brevundimonas_20 7.191409e-03 19 NA
## Delftia_21 1.743665e-02 21 NA
## Methylobacterium_Methylorubrum_22 1.023565e-02 20 NA
## Methyloversatilis_23 4.577730e-03 19 NA
## Bradyrhizobium_24 1.873942e-02 27 NA
## Comamonas_25 2.631675e-03 10 NA
## Methylobacterium_Methylorubrum_26 8.576104e-03 17 NA
## Noviherbaspirillum_suwonense_27 9.789757e-03 22 NA
## Brevundimonas_28 3.081368e-03 12 NA
## Xanthobacter_autotrophicus_29 2.588114e-03 17 NA
## Dolosigranulum_pigrum_30 5.838612e-03 21 NA
## Lactococcus_31 4.620817e-03 17 NA
## Methylobacterium_Methylorubrum_32 4.634599e-03 10 NA
## Pseudomonas_33 8.815062e-03 20 NA
## Streptococcus_34 3.945675e-03 13 NA
## Corynebacterium_35 6.358113e-03 12 NA
## Bosea_36 4.360623e-03 17 NA
## Moraxella_37 4.422753e-03 15 NA
## Bacteriovorax_stolpii_38 2.521520e-03 15 NA
## Bosea_vestrisii_39 1.640185e-03 8 NA
## Phenylobacterium_muchangponense_40 1.853456e-03 13 NA
## Rhodopseudomonas_41 4.630046e-03 13 NA
## Sphingomonas_koreensis_42 1.756416e-03 18 NA
## Achromobacter_43 3.173297e-03 16 NA
## Corynebacterium_44 5.036577e-03 11 NA
## Pseudomonas_japonica_45 2.926962e-03 17 NA
## Aquabacterium_parvum_46 2.394551e-03 15 NA
## Aquabacterium_47 1.199956e-03 7 NA
## Sphingopyxis_48 1.027747e-03 8 NA
## Thiobacillus_thioparus_49 1.156326e-03 13 NA
## Thermus_brockianus_50 9.803232e-04 5 NA
## Staphylococcus_51 1.288305e-03 4 NA
## Aquabacterium_52 1.288473e-03 10 NA
## Brevundimonas_53 1.211565e-03 6 NA
## Haemophilus_54 1.868575e-03 2 NA
## Burkholderia_Caballeronia_Paraburkholderia_55 4.099069e-03 12 NA
## Cloacibacterium_56 1.076944e-03 10 NA
## Blastococcus_57 2.135012e-03 10 NA
## Meiothermus_58 1.502747e-03 9 NA
## Corynebacterium_59 6.413893e-04 4 NA
## Alcaligenes_60 7.473932e-04 8 NA
## Methylobacterium_Methylorubrum_61 3.479678e-03 14 NA
## Corynebacterium_62 2.375905e-03 11 NA
## Neisseria_63 1.624652e-03 5 NA
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_64 3.016940e-03 12 NA
## Brevundimonas_kwangchunensis_65 1.256267e-03 7 NA
## Ralstonia_66 2.097186e-03 10 NA
## Cupriavidus_gilardii_67 7.389218e-04 7 NA
## Streptococcus_69 2.880323e-03 6 NA
## Enhydrobacter_70 1.413665e-03 9 NA
## Microbacteriaceae_71 5.021637e-04 5 NA
## Afipia_72 2.617811e-03 9 NA
## Bosea_73 6.876345e-04 7 NA
## Brevundimonas_74 1.969040e-03 8 NA
## Brachymonas_75 5.356044e-04 5 NA
## Fluviicola_76 5.439258e-04 7 NA
## Skermanella_aerolata_77 9.277934e-04 7 NA
## Cupriavidus_78 5.662280e-04 4 NA
## Acinetobacter_79 1.828253e-03 9 NA
## Pseudarthrobacter_80 1.116450e-03 7 NA
## Sphingobium_81 9.504694e-04 4 NA
## Limnohabitans_82 2.149204e-03 4 NA
## Bacillus_83 1.258702e-03 2 NA
## Sphingomonadaceae_84 5.693857e-04 8 NA
## Corynebacterium_matruchotii_85 4.487368e-04 2 NA
## Pseudomonas_86 6.847992e-04 6 NA
## Psychroglaciecola_87 1.486552e-03 8 NA
## Acinetobacter_88 6.740599e-04 6 NA
## Proteus_89 5.695317e-04 7 NA
## Streptococcus_90 3.061191e-03 6 NA
## Methylobacterium_Methylorubrum_91 7.222484e-04 4 NA
## Caulobacteraceae_92 4.405513e-04 4 NA
## Escherichia/Shigella_93 6.661923e-04 9 NA
## Halomonas_94 1.100791e-03 10 NA
## Nocardioides_95 2.243128e-03 5 NA
## Bosea_96 8.935583e-04 5 NA
## Micrococcus_97 1.401391e-03 7 NA
## Methylobacillus_98 3.603298e-04 7 NA
## Acinetobacter_100 2.137697e-04 3 NA
## Phenylobacterium_mobile_101 1.123234e-03 7 NA
## Anoxybacillus_102 4.118755e-04 9 NA
## Variovorax_paradoxus_103 5.553379e-04 3 NA
## Caulobacter_104 1.718234e-03 8 NA
## Pseudomonas_105 3.586539e-04 5 NA
## Tepidiphilus_succinatimandens_106 2.995392e-04 4 NA
## Haliangium_107 3.043686e-04 7 NA
## Dietzia_109 5.417954e-04 4 NA
## Reyranella_massiliensis_110 2.708226e-04 6 NA
## Massilia_111 1.190506e-03 8 NA
## Methylopila_oligotropha_112 4.412900e-04 2 NA
## Haemophilus_114 8.460066e-04 6 NA
## Stenotrophomonas_115 2.566185e-04 4 NA
## Sphingomonas_koreensis_116 6.037061e-04 6 NA
## Asticcacaulis_excentricus_117 2.465046e-04 6 NA
## Neisseria_118 7.104776e-04 5 NA
## Actinomyces_119 7.964368e-04 6 NA
## Sphingobium_yanoikuyae_120 2.671516e-04 4 NA
## Ralstonia_121 6.265734e-04 3 NA
## Sphingopyxis_122 4.629217e-04 4 NA
## Yonghaparkia_123 2.531714e-04 4 NA
## Brevundimonas_124 2.240993e-04 3 NA
## Brevundimonas_126 5.481631e-04 8 NA
## Anoxybacillus_127 7.565009e-04 4 NA
## Tepidimonas_128 2.695019e-04 5 NA
## Stenotrophomonas_129 5.516733e-04 5 NA
## Providencia_130 1.438536e-04 3 NA
## Blastomonas_131 2.825748e-04 4 NA
## PMMR1_132 3.711265e-04 5 NA
## Haemophilus_133 1.068683e-03 2 NA
## Mesorhizobium_134 2.277644e-03 5 NA
## Brevundimonas_135 8.638955e-04 4 NA
## Sphingobium_137 1.138941e-03 6 NA
## Vulcaniibacterium_139 4.756674e-04 5 NA
## Klenkia_140 5.342252e-04 4 NA
## Sphingomonas_141 4.960657e-04 5 NA
## Lawsonella_clevelandensis_143 2.501346e-03 2 NA
## Ellin6055_144 2.140471e-04 2 NA
## Pseudomonas_145 4.740453e-04 4 NA
## Devosia_neptuniae_146 2.344607e-04 4 NA
## Janibacter_147 2.727322e-04 3 NA
## Devosia_148 3.730491e-04 4 NA
## Brevundimonas_149 1.115734e-03 5 NA
## Candidatus_Paracaedibacter_150 2.866392e-04 3 NA
## Paucibacter_151 2.361778e-03 2 NA
## Meiothermus_152 3.699185e-04 4 NA
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_153 2.141639e-03 3 NA
## Pseudomonas_154 8.996049e-04 5 NA
## Rhodopseudomonas_155 4.514538e-04 5 NA
## Rhodopseudomonas_156 5.037061e-04 4 NA
## Frankiales_158 2.928379e-04 5 NA
## Novosphingobium_159 1.034461e-03 4 NA
## Sphingosinicella_163 5.277515e-04 3 NA
## Flavihumibacter_165 9.101061e-05 2 NA
## Duganella_166 9.790435e-04 5 NA
## Granulicatella_167 2.415129e-04 3 NA
## Sphingoaurantiacus_polygranulatus_168 3.988027e-04 4 NA
## Caulobacter_169 2.436415e-04 4 NA
## Finegoldia_171 1.403796e-03 3 NA
## Acinetobacter_lwoffii_175 4.388196e-04 6 NA
## Methylopilaceae_176 2.930608e-04 5 NA
## Halomonas_178 2.424020e-04 2 NA
## Abiotrophia_defectiva_181 2.274780e-04 2 NA
## Caulobacteraceae_182 1.215108e-04 2 NA
## Sphingomonas_184 6.956600e-04 2 NA
## Herbiconiux_185 2.414732e-04 3 NA
## Phreatobacter_oligotrophus_186 1.427478e-04 3 NA
## Alphaproteobacteria_187 1.187981e-04 2 NA
## Leifsonia_188 3.335420e-04 3 NA
## Microvirga_189 2.545846e-04 2 NA
## Porphyromonas_gingivalis_193 3.186010e-04 3 NA
## Bdellovibrio_194 2.998722e-04 5 NA
## Streptococcus_195 4.069771e-04 2 NA
## Enterobacterales_198 7.334188e-04 2 NA
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_199 6.336171e-04 4 NA
## Brachybacterium_200 2.152676e-03 3 NA
## Burkholderia_Caballeronia_Paraburkholderia_201 1.715866e-04 2 NA
## Galbitalea_203 3.198893e-04 2 NA
## Sphingobium_205 2.113321e-04 2 NA
## Paracoccus_206 2.058113e-04 3 NA
## Pyrinomonas_207 1.174753e-04 4 NA
## Brevibacillus_208 1.127366e-04 4 NA
## Luteimonas_209 2.274547e-04 3 NA
## Bosea_211 7.081637e-05 2 NA
## Micrococcaceae_212 3.401229e-04 2 NA
## Gemella_213 2.162752e-04 3 NA
## Massilia_214 2.700437e-04 2 NA
## Streptococcus_216 1.398308e-04 2 NA
## Patulibacter_minatonensis_217 1.576705e-04 4 NA
## Acidovorax_218 1.939312e-04 2 NA
## Alloprevotella_219 1.955154e-04 3 NA
## Novispirillum_220 1.131025e-04 3 NA
## Rhodococcus_221 2.200130e-04 3 NA
## Fimbriiglobus_223 2.148883e-04 2 NA
## Gammaproteobacteria_226 1.566046e-04 3 NA
## Aquabacterium_228 1.600243e-04 2 NA
## Acetobacteraceae_230 3.047097e-04 3 NA
## Bosea_231 5.960337e-05 2 NA
## Tepidiphilus_232 1.995040e-04 3 NA
## Aerococcus_233 1.563555e-04 3 NA
## Geodermatophilus_234 1.965957e-04 2 NA
## Limnobacter_thiooxidans_236 1.601710e-04 2 NA
## Methylobacterium_Methylorubrum_240 4.744278e-04 2 NA
## Hansschlegelia_242 1.968406e-04 2 NA
## Roseomonas_cervicalis_243 1.100142e-04 3 NA
## Legionella_lytica_244 1.189608e-04 3 NA
## Klebsiella_245 2.269239e-04 3 NA
## Methylobacterium_Methylorubrum_246 3.654402e-04 2 NA
## Prevotella_histicola_247 2.056466e-04 2 NA
## Prevotella_248 9.410058e-05 2 NA
## Porphyromonas_254 1.018007e-04 2 NA
## Belnapia_rosea_259 1.077757e-03 2 NA
## Methylophilus_263 1.307617e-04 3 NA
## Staphylococcus_264 2.302541e-04 2 NA
## Methylobacterium_Methylorubrum_265 9.333568e-05 2 NA
## Turicella_otitidis_268 1.591035e-04 2 NA
## Quadrisphaera_271 1.421203e-04 2 NA
## Caulobacter_272 1.589604e-04 2 NA
## Stenotrophomonas_277 7.202095e-05 2 NA
## Frankiales_280 9.085665e-05 2 NA
## Stenotrophomonas_282 6.757826e-04 3 NA
## Phenylobacterium_286 9.862635e-05 2 NA
## Devosia_288 3.587238e-04 3 NA
## Pseudoxanthomonas_mexicana_289 1.165694e-04 2 NA
## Clostridium_sensu_stricto_1_293 2.822203e-04 2 NA
## Mesorhizobium_294 2.055011e-04 2 NA
## KD4_96_297 1.355725e-04 2 NA
## Kocuria_299 1.173779e-04 2 NA
## Streptococcus_303 2.532132e-04 2 NA
## Novosphingobium_311 4.888161e-04 2 NA
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_314 1.579087e-04 2 NA
## Vulcaniibacterium_thermophilum_321 1.127475e-04 2 NA
## Kocuria_330 1.266236e-04 2 NA
## DSSD61_338 1.593818e-04 2 NA
## Dietzia_timorensis_339 1.424384e-04 2 NA
## Ruminiclostridium_341 4.361371e-04 2 NA
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_343 2.247268e-04 2 NA
## Paenarthrobacter_344 2.655507e-04 2 NA
## Candidatus_Paracaedibacter_347 1.317488e-04 3 NA
## Veillonella_348 1.659913e-04 2 NA
## Cnuella_349 1.373447e-04 3 NA
## UCG_005_354 8.460448e-05 2 NA
## Cellulomonas_360 2.327451e-04 2 NA
## Acetitomaculum_362 1.390598e-04 3 NA
## Thermomonas_hydrothermalis_363 8.074141e-05 2 NA
## Ensifer_365 1.524687e-04 2 NA
## Sphingomonas_367 1.872970e-04 2 NA
## Burkholderia_Caballeronia_Paraburkholderia_374 1.141369e-04 2 NA
## Pedobacter_381 1.886407e-04 3 NA
## Hydrocarboniphaga_389 7.451606e-04 3 NA
## Pseudoxanthomonas_447 4.204920e-05 2 NA
## p.prev
## Tepidimonas_1 1.895346e-05
## Curvibacter_2 9.999810e-01
## Thermus_3 2.452585e-02
## Schlegelella_4 9.999595e-01
## Thermus_5 1.458329e-06
## Caulobacter_6 9.999595e-01
## Comamonadaceae_7 9.999595e-01
## Sphingomonas_8 3.336824e-06
## Bosea_9 9.999595e-01
## Thermus_thermophilus_10 3.067459e-10
## Sphingobium_11 9.984145e-01
## Pseudomonas_12 1.641610e-01
## Methylobacterium_Methylorubrum_13 4.139547e-05
## Ottowia_14 9.994154e-01
## Azospirillum_15 9.999168e-01
## Meiothermus_silvanus_16 9.999595e-01
## Staphylococcus_17 7.112495e-01
## Nitriliruptoraceae_18 2.412216e-02
## Paucibacter_19 2.252986e-01
## Brevundimonas_20 9.999595e-01
## Delftia_21 4.013900e-03
## Methylobacterium_Methylorubrum_22 2.404425e-05
## Methyloversatilis_23 9.999595e-01
## Bradyrhizobium_24 1.702994e-05
## Comamonas_25 9.976265e-01
## Methylobacterium_Methylorubrum_26 8.489774e-06
## Noviherbaspirillum_suwonense_27 1.822152e-04
## Brevundimonas_28 9.993350e-01
## Xanthobacter_autotrophicus_29 9.985774e-01
## Dolosigranulum_pigrum_30 7.954082e-01
## Lactococcus_31 4.602183e-10
## Methylobacterium_Methylorubrum_32 6.606553e-02
## Pseudomonas_33 4.214956e-02
## Streptococcus_34 5.392733e-03
## Corynebacterium_35 1.725643e-01
## Bosea_36 4.794449e-01
## Moraxella_37 9.129339e-01
## Bacteriovorax_stolpii_38 9.994154e-01
## Bosea_vestrisii_39 9.921676e-01
## Phenylobacterium_muchangponense_40 9.996594e-01
## Rhodopseudomonas_41 7.094566e-04
## Sphingomonas_koreensis_42 9.149279e-01
## Achromobacter_43 9.996850e-01
## Corynebacterium_44 9.251069e-01
## Pseudomonas_japonica_45 8.489774e-06
## Aquabacterium_parvum_46 9.955673e-01
## Aquabacterium_47 9.861426e-01
## Sphingopyxis_48 9.921676e-01
## Thiobacillus_thioparus_49 9.996594e-01
## Thermus_brockianus_50 2.701342e-03
## Staphylococcus_51 6.739349e-01
## Aquabacterium_52 9.976265e-01
## Brevundimonas_53 9.758778e-01
## Haemophilus_54 6.894049e-02
## Burkholderia_Caballeronia_Paraburkholderia_55 1.725643e-01
## Cloacibacterium_56 2.010901e-01
## Blastococcus_57 1.455012e-02
## Meiothermus_58 9.599675e-01
## Corynebacterium_59 3.163152e-01
## Alcaligenes_60 9.921676e-01
## Methylobacterium_Methylorubrum_61 1.933266e-02
## Corynebacterium_62 1.122213e-01
## Neisseria_63 2.701342e-03
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_64 1.348270e-02
## Brevundimonas_kwangchunensis_65 2.514547e-04
## Ralstonia_66 6.606553e-02
## Cupriavidus_gilardii_67 9.861426e-01
## Streptococcus_69 8.483750e-01
## Enhydrobacter_70 3.407175e-02
## Microbacteriaceae_71 4.514882e-01
## Afipia_72 1.295108e-01
## Bosea_73 9.861426e-01
## Brevundimonas_74 2.334002e-01
## Brachymonas_75 9.586477e-01
## Fluviicola_76 9.861426e-01
## Skermanella_aerolata_77 4.651912e-03
## Cupriavidus_78 9.301289e-01
## Acinetobacter_79 3.407175e-02
## Pseudarthrobacter_80 2.514547e-04
## Sphingobium_81 8.272859e-03
## Limnohabitans_82 8.078204e-02
## Bacillus_83 3.773585e-01
## Sphingomonadaceae_84 4.978753e-01
## Corynebacterium_matruchotii_85 6.894049e-02
## Pseudomonas_86 9.758778e-01
## Psychroglaciecola_87 1.457344e-02
## Acinetobacter_88 7.968958e-02
## Proteus_89 9.861426e-01
## Streptococcus_90 7.968958e-02
## Methylobacterium_Methylorubrum_91 8.272859e-03
## Caulobacteraceae_92 6.739349e-01
## Escherichia/Shigella_93 3.308276e-01
## Halomonas_94 6.606553e-02
## Nocardioides_95 1.659935e-01
## Bosea_96 2.701342e-03
## Micrococcus_97 1.476542e-01
## Methylobacillus_98 9.861426e-01
## Acinetobacter_100 8.835482e-01
## Phenylobacterium_mobile_101 3.535453e-02
## Anoxybacillus_102 1.895021e-05
## Variovorax_paradoxus_103 2.433194e-02
## Caulobacter_104 1.457344e-02
## Pseudomonas_105 9.586477e-01
## Tepidiphilus_succinatimandens_106 9.301289e-01
## Haliangium_107 9.861426e-01
## Dietzia_109 8.078204e-02
## Reyranella_massiliensis_110 9.758778e-01
## Massilia_111 7.383803e-02
## Methylopila_oligotropha_112 6.894049e-02
## Haemophilus_114 7.968958e-02
## Stenotrophomonas_115 6.739349e-01
## Sphingomonas_koreensis_116 9.758778e-01
## Asticcacaulis_excentricus_117 9.758778e-01
## Neisseria_118 1.659935e-01
## Actinomyces_119 5.756639e-01
## Sphingobium_yanoikuyae_120 9.301289e-01
## Ralstonia_121 5.417058e-01
## Sphingopyxis_122 9.301289e-01
## Yonghaparkia_123 8.272859e-03
## Brevundimonas_124 8.835482e-01
## Brevundimonas_126 9.921676e-01
## Anoxybacillus_127 8.078204e-02
## Tepidimonas_128 9.586477e-01
## Stenotrophomonas_129 1.659935e-01
## Providencia_130 8.835482e-01
## Blastomonas_131 9.301289e-01
## PMMR1_132 9.586477e-01
## Haemophilus_133 3.773585e-01
## Mesorhizobium_134 1.659935e-01
## Brevundimonas_135 8.078204e-02
## Sphingobium_137 7.968958e-02
## Vulcaniibacterium_139 9.586477e-01
## Klenkia_140 8.272859e-03
## Sphingomonas_141 1.659935e-01
## Lawsonella_clevelandensis_143 8.084180e-01
## Ellin6055_144 3.773585e-01
## Pseudomonas_145 8.272859e-03
## Devosia_neptuniae_146 8.272859e-03
## Janibacter_147 2.433194e-02
## Devosia_148 9.301289e-01
## Brevundimonas_149 1.659935e-01
## Candidatus_Paracaedibacter_150 8.835482e-01
## Paucibacter_151 8.084180e-01
## Meiothermus_152 8.272859e-03
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_153 5.417058e-01
## Pseudomonas_154 1.659935e-01
## Rhodopseudomonas_155 3.326027e-02
## Rhodopseudomonas_156 8.078204e-02
## Frankiales_158 1.659935e-01
## Novosphingobium_159 8.078204e-02
## Sphingosinicella_163 8.835482e-01
## Flavihumibacter_165 8.084180e-01
## Duganella_166 3.326027e-02
## Granulicatella_167 1.824895e-01
## Sphingoaurantiacus_polygranulatus_168 8.272859e-03
## Caulobacter_169 8.272859e-03
## Finegoldia_171 1.824895e-01
## Acinetobacter_lwoffii_175 7.968958e-02
## Methylopilaceae_176 2.701342e-03
## Halomonas_178 3.773585e-01
## Abiotrophia_defectiva_181 6.894049e-02
## Caulobacteraceae_182 8.084180e-01
## Sphingomonas_184 8.084180e-01
## Herbiconiux_185 2.433194e-02
## Phreatobacter_oligotrophus_186 8.835482e-01
## Alphaproteobacteria_187 8.084180e-01
## Leifsonia_188 2.433194e-02
## Microvirga_189 6.894049e-02
## Porphyromonas_gingivalis_193 2.433194e-02
## Bdellovibrio_194 9.586477e-01
## Streptococcus_195 3.773585e-01
## Enterobacterales_198 6.894049e-02
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_199 8.078204e-02
## Brachybacterium_200 5.417058e-01
## Burkholderia_Caballeronia_Paraburkholderia_201 6.894049e-02
## Galbitalea_203 6.894049e-02
## Sphingobium_205 8.084180e-01
## Paracoccus_206 5.417058e-01
## Pyrinomonas_207 9.301289e-01
## Brevibacillus_208 8.272859e-03
## Luteimonas_209 2.433194e-02
## Bosea_211 8.084180e-01
## Micrococcaceae_212 3.773585e-01
## Gemella_213 1.824895e-01
## Massilia_214 6.894049e-02
## Streptococcus_216 3.773585e-01
## Patulibacter_minatonensis_217 8.272859e-03
## Acidovorax_218 6.894049e-02
## Alloprevotella_219 1.824895e-01
## Novispirillum_220 8.835482e-01
## Rhodococcus_221 5.417058e-01
## Fimbriiglobus_223 6.894049e-02
## Gammaproteobacteria_226 8.835482e-01
## Aquabacterium_228 6.894049e-02
## Acetobacteraceae_230 1.824895e-01
## Bosea_231 8.084180e-01
## Tepidiphilus_232 8.835482e-01
## Aerococcus_233 2.433194e-02
## Geodermatophilus_234 3.773585e-01
## Limnobacter_thiooxidans_236 3.773585e-01
## Methylobacterium_Methylorubrum_240 6.894049e-02
## Hansschlegelia_242 6.894049e-02
## Roseomonas_cervicalis_243 8.835482e-01
## Legionella_lytica_244 8.835482e-01
## Klebsiella_245 1.824895e-01
## Methylobacterium_Methylorubrum_246 6.894049e-02
## Prevotella_histicola_247 6.894049e-02
## Prevotella_248 6.894049e-02
## Porphyromonas_254 6.894049e-02
## Belnapia_rosea_259 3.773585e-01
## Methylophilus_263 2.433194e-02
## Staphylococcus_264 3.773585e-01
## Methylobacterium_Methylorubrum_265 8.084180e-01
## Turicella_otitidis_268 6.894049e-02
## Quadrisphaera_271 6.894049e-02
## Caulobacter_272 8.084180e-01
## Stenotrophomonas_277 3.773585e-01
## Frankiales_280 3.773585e-01
## Stenotrophomonas_282 5.417058e-01
## Phenylobacterium_286 6.894049e-02
## Devosia_288 2.433194e-02
## Pseudoxanthomonas_mexicana_289 6.894049e-02
## Clostridium_sensu_stricto_1_293 3.773585e-01
## Mesorhizobium_294 3.773585e-01
## KD4_96_297 6.894049e-02
## Kocuria_299 6.894049e-02
## Streptococcus_303 6.894049e-02
## Novosphingobium_311 8.084180e-01
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_314 6.894049e-02
## Vulcaniibacterium_thermophilum_321 8.084180e-01
## Kocuria_330 6.894049e-02
## DSSD61_338 3.773585e-01
## Dietzia_timorensis_339 3.773585e-01
## Ruminiclostridium_341 3.773585e-01
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_343 3.773585e-01
## Paenarthrobacter_344 3.773585e-01
## Candidatus_Paracaedibacter_347 2.433194e-02
## Veillonella_348 6.894049e-02
## Cnuella_349 2.433194e-02
## UCG_005_354 6.894049e-02
## Cellulomonas_360 3.773585e-01
## Acetitomaculum_362 2.433194e-02
## Thermomonas_hydrothermalis_363 6.894049e-02
## Ensifer_365 6.894049e-02
## Sphingomonas_367 6.894049e-02
## Burkholderia_Caballeronia_Paraburkholderia_374 6.894049e-02
## Pedobacter_381 2.433194e-02
## Hydrocarboniphaga_389 5.417058e-01
## Pseudoxanthomonas_447 6.894049e-02
## p
## Tepidimonas_1 1.895346e-05
## Curvibacter_2 9.999810e-01
## Thermus_3 2.452585e-02
## Schlegelella_4 9.999595e-01
## Thermus_5 1.458329e-06
## Caulobacter_6 9.999595e-01
## Comamonadaceae_7 9.999595e-01
## Sphingomonas_8 3.336824e-06
## Bosea_9 9.999595e-01
## Thermus_thermophilus_10 3.067459e-10
## Sphingobium_11 9.984145e-01
## Pseudomonas_12 1.641610e-01
## Methylobacterium_Methylorubrum_13 4.139547e-05
## Ottowia_14 9.994154e-01
## Azospirillum_15 9.999168e-01
## Meiothermus_silvanus_16 9.999595e-01
## Staphylococcus_17 7.112495e-01
## Nitriliruptoraceae_18 2.412216e-02
## Paucibacter_19 2.252986e-01
## Brevundimonas_20 9.999595e-01
## Delftia_21 4.013900e-03
## Methylobacterium_Methylorubrum_22 2.404425e-05
## Methyloversatilis_23 9.999595e-01
## Bradyrhizobium_24 1.702994e-05
## Comamonas_25 9.976265e-01
## Methylobacterium_Methylorubrum_26 8.489774e-06
## Noviherbaspirillum_suwonense_27 1.822152e-04
## Brevundimonas_28 9.993350e-01
## Xanthobacter_autotrophicus_29 9.985774e-01
## Dolosigranulum_pigrum_30 7.954082e-01
## Lactococcus_31 4.602183e-10
## Methylobacterium_Methylorubrum_32 6.606553e-02
## Pseudomonas_33 4.214956e-02
## Streptococcus_34 5.392733e-03
## Corynebacterium_35 1.725643e-01
## Bosea_36 4.794449e-01
## Moraxella_37 9.129339e-01
## Bacteriovorax_stolpii_38 9.994154e-01
## Bosea_vestrisii_39 9.921676e-01
## Phenylobacterium_muchangponense_40 9.996594e-01
## Rhodopseudomonas_41 7.094566e-04
## Sphingomonas_koreensis_42 9.149279e-01
## Achromobacter_43 9.996850e-01
## Corynebacterium_44 9.251069e-01
## Pseudomonas_japonica_45 8.489774e-06
## Aquabacterium_parvum_46 9.955673e-01
## Aquabacterium_47 9.861426e-01
## Sphingopyxis_48 9.921676e-01
## Thiobacillus_thioparus_49 9.996594e-01
## Thermus_brockianus_50 2.701342e-03
## Staphylococcus_51 6.739349e-01
## Aquabacterium_52 9.976265e-01
## Brevundimonas_53 9.758778e-01
## Haemophilus_54 6.894049e-02
## Burkholderia_Caballeronia_Paraburkholderia_55 1.725643e-01
## Cloacibacterium_56 2.010901e-01
## Blastococcus_57 1.455012e-02
## Meiothermus_58 9.599675e-01
## Corynebacterium_59 3.163152e-01
## Alcaligenes_60 9.921676e-01
## Methylobacterium_Methylorubrum_61 1.933266e-02
## Corynebacterium_62 1.122213e-01
## Neisseria_63 2.701342e-03
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_64 1.348270e-02
## Brevundimonas_kwangchunensis_65 2.514547e-04
## Ralstonia_66 6.606553e-02
## Cupriavidus_gilardii_67 9.861426e-01
## Streptococcus_69 8.483750e-01
## Enhydrobacter_70 3.407175e-02
## Microbacteriaceae_71 4.514882e-01
## Afipia_72 1.295108e-01
## Bosea_73 9.861426e-01
## Brevundimonas_74 2.334002e-01
## Brachymonas_75 9.586477e-01
## Fluviicola_76 9.861426e-01
## Skermanella_aerolata_77 4.651912e-03
## Cupriavidus_78 9.301289e-01
## Acinetobacter_79 3.407175e-02
## Pseudarthrobacter_80 2.514547e-04
## Sphingobium_81 8.272859e-03
## Limnohabitans_82 8.078204e-02
## Bacillus_83 3.773585e-01
## Sphingomonadaceae_84 4.978753e-01
## Corynebacterium_matruchotii_85 6.894049e-02
## Pseudomonas_86 9.758778e-01
## Psychroglaciecola_87 1.457344e-02
## Acinetobacter_88 7.968958e-02
## Proteus_89 9.861426e-01
## Streptococcus_90 7.968958e-02
## Methylobacterium_Methylorubrum_91 8.272859e-03
## Caulobacteraceae_92 6.739349e-01
## Escherichia/Shigella_93 3.308276e-01
## Halomonas_94 6.606553e-02
## Nocardioides_95 1.659935e-01
## Bosea_96 2.701342e-03
## Micrococcus_97 1.476542e-01
## Methylobacillus_98 9.861426e-01
## Acinetobacter_100 8.835482e-01
## Phenylobacterium_mobile_101 3.535453e-02
## Anoxybacillus_102 1.895021e-05
## Variovorax_paradoxus_103 2.433194e-02
## Caulobacter_104 1.457344e-02
## Pseudomonas_105 9.586477e-01
## Tepidiphilus_succinatimandens_106 9.301289e-01
## Haliangium_107 9.861426e-01
## Dietzia_109 8.078204e-02
## Reyranella_massiliensis_110 9.758778e-01
## Massilia_111 7.383803e-02
## Methylopila_oligotropha_112 6.894049e-02
## Haemophilus_114 7.968958e-02
## Stenotrophomonas_115 6.739349e-01
## Sphingomonas_koreensis_116 9.758778e-01
## Asticcacaulis_excentricus_117 9.758778e-01
## Neisseria_118 1.659935e-01
## Actinomyces_119 5.756639e-01
## Sphingobium_yanoikuyae_120 9.301289e-01
## Ralstonia_121 5.417058e-01
## Sphingopyxis_122 9.301289e-01
## Yonghaparkia_123 8.272859e-03
## Brevundimonas_124 8.835482e-01
## Brevundimonas_126 9.921676e-01
## Anoxybacillus_127 8.078204e-02
## Tepidimonas_128 9.586477e-01
## Stenotrophomonas_129 1.659935e-01
## Providencia_130 8.835482e-01
## Blastomonas_131 9.301289e-01
## PMMR1_132 9.586477e-01
## Haemophilus_133 3.773585e-01
## Mesorhizobium_134 1.659935e-01
## Brevundimonas_135 8.078204e-02
## Sphingobium_137 7.968958e-02
## Vulcaniibacterium_139 9.586477e-01
## Klenkia_140 8.272859e-03
## Sphingomonas_141 1.659935e-01
## Lawsonella_clevelandensis_143 8.084180e-01
## Ellin6055_144 3.773585e-01
## Pseudomonas_145 8.272859e-03
## Devosia_neptuniae_146 8.272859e-03
## Janibacter_147 2.433194e-02
## Devosia_148 9.301289e-01
## Brevundimonas_149 1.659935e-01
## Candidatus_Paracaedibacter_150 8.835482e-01
## Paucibacter_151 8.084180e-01
## Meiothermus_152 8.272859e-03
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_153 5.417058e-01
## Pseudomonas_154 1.659935e-01
## Rhodopseudomonas_155 3.326027e-02
## Rhodopseudomonas_156 8.078204e-02
## Frankiales_158 1.659935e-01
## Novosphingobium_159 8.078204e-02
## Sphingosinicella_163 8.835482e-01
## Flavihumibacter_165 8.084180e-01
## Duganella_166 3.326027e-02
## Granulicatella_167 1.824895e-01
## Sphingoaurantiacus_polygranulatus_168 8.272859e-03
## Caulobacter_169 8.272859e-03
## Finegoldia_171 1.824895e-01
## Acinetobacter_lwoffii_175 7.968958e-02
## Methylopilaceae_176 2.701342e-03
## Halomonas_178 3.773585e-01
## Abiotrophia_defectiva_181 6.894049e-02
## Caulobacteraceae_182 8.084180e-01
## Sphingomonas_184 8.084180e-01
## Herbiconiux_185 2.433194e-02
## Phreatobacter_oligotrophus_186 8.835482e-01
## Alphaproteobacteria_187 8.084180e-01
## Leifsonia_188 2.433194e-02
## Microvirga_189 6.894049e-02
## Porphyromonas_gingivalis_193 2.433194e-02
## Bdellovibrio_194 9.586477e-01
## Streptococcus_195 3.773585e-01
## Enterobacterales_198 6.894049e-02
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_199 8.078204e-02
## Brachybacterium_200 5.417058e-01
## Burkholderia_Caballeronia_Paraburkholderia_201 6.894049e-02
## Galbitalea_203 6.894049e-02
## Sphingobium_205 8.084180e-01
## Paracoccus_206 5.417058e-01
## Pyrinomonas_207 9.301289e-01
## Brevibacillus_208 8.272859e-03
## Luteimonas_209 2.433194e-02
## Bosea_211 8.084180e-01
## Micrococcaceae_212 3.773585e-01
## Gemella_213 1.824895e-01
## Massilia_214 6.894049e-02
## Streptococcus_216 3.773585e-01
## Patulibacter_minatonensis_217 8.272859e-03
## Acidovorax_218 6.894049e-02
## Alloprevotella_219 1.824895e-01
## Novispirillum_220 8.835482e-01
## Rhodococcus_221 5.417058e-01
## Fimbriiglobus_223 6.894049e-02
## Gammaproteobacteria_226 8.835482e-01
## Aquabacterium_228 6.894049e-02
## Acetobacteraceae_230 1.824895e-01
## Bosea_231 8.084180e-01
## Tepidiphilus_232 8.835482e-01
## Aerococcus_233 2.433194e-02
## Geodermatophilus_234 3.773585e-01
## Limnobacter_thiooxidans_236 3.773585e-01
## Methylobacterium_Methylorubrum_240 6.894049e-02
## Hansschlegelia_242 6.894049e-02
## Roseomonas_cervicalis_243 8.835482e-01
## Legionella_lytica_244 8.835482e-01
## Klebsiella_245 1.824895e-01
## Methylobacterium_Methylorubrum_246 6.894049e-02
## Prevotella_histicola_247 6.894049e-02
## Prevotella_248 6.894049e-02
## Porphyromonas_254 6.894049e-02
## Belnapia_rosea_259 3.773585e-01
## Methylophilus_263 2.433194e-02
## Staphylococcus_264 3.773585e-01
## Methylobacterium_Methylorubrum_265 8.084180e-01
## Turicella_otitidis_268 6.894049e-02
## Quadrisphaera_271 6.894049e-02
## Caulobacter_272 8.084180e-01
## Stenotrophomonas_277 3.773585e-01
## Frankiales_280 3.773585e-01
## Stenotrophomonas_282 5.417058e-01
## Phenylobacterium_286 6.894049e-02
## Devosia_288 2.433194e-02
## Pseudoxanthomonas_mexicana_289 6.894049e-02
## Clostridium_sensu_stricto_1_293 3.773585e-01
## Mesorhizobium_294 3.773585e-01
## KD4_96_297 6.894049e-02
## Kocuria_299 6.894049e-02
## Streptococcus_303 6.894049e-02
## Novosphingobium_311 8.084180e-01
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_314 6.894049e-02
## Vulcaniibacterium_thermophilum_321 8.084180e-01
## Kocuria_330 6.894049e-02
## DSSD61_338 3.773585e-01
## Dietzia_timorensis_339 3.773585e-01
## Ruminiclostridium_341 3.773585e-01
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_343 3.773585e-01
## Paenarthrobacter_344 3.773585e-01
## Candidatus_Paracaedibacter_347 2.433194e-02
## Veillonella_348 6.894049e-02
## Cnuella_349 2.433194e-02
## UCG_005_354 6.894049e-02
## Cellulomonas_360 3.773585e-01
## Acetitomaculum_362 2.433194e-02
## Thermomonas_hydrothermalis_363 6.894049e-02
## Ensifer_365 6.894049e-02
## Sphingomonas_367 6.894049e-02
## Burkholderia_Caballeronia_Paraburkholderia_374 6.894049e-02
## Pedobacter_381 2.433194e-02
## Hydrocarboniphaga_389 5.417058e-01
## Pseudoxanthomonas_447 6.894049e-02
## not.contaminant
## Tepidimonas_1 TRUE
## Curvibacter_2 FALSE
## Thermus_3 TRUE
## Schlegelella_4 FALSE
## Thermus_5 TRUE
## Caulobacter_6 FALSE
## Comamonadaceae_7 FALSE
## Sphingomonas_8 TRUE
## Bosea_9 FALSE
## Thermus_thermophilus_10 TRUE
## Sphingobium_11 FALSE
## Pseudomonas_12 TRUE
## Methylobacterium_Methylorubrum_13 TRUE
## Ottowia_14 FALSE
## Azospirillum_15 FALSE
## Meiothermus_silvanus_16 FALSE
## Staphylococcus_17 FALSE
## Nitriliruptoraceae_18 TRUE
## Paucibacter_19 TRUE
## Brevundimonas_20 FALSE
## Delftia_21 TRUE
## Methylobacterium_Methylorubrum_22 TRUE
## Methyloversatilis_23 FALSE
## Bradyrhizobium_24 TRUE
## Comamonas_25 FALSE
## Methylobacterium_Methylorubrum_26 TRUE
## Noviherbaspirillum_suwonense_27 TRUE
## Brevundimonas_28 FALSE
## Xanthobacter_autotrophicus_29 FALSE
## Dolosigranulum_pigrum_30 FALSE
## Lactococcus_31 TRUE
## Methylobacterium_Methylorubrum_32 TRUE
## Pseudomonas_33 TRUE
## Streptococcus_34 TRUE
## Corynebacterium_35 TRUE
## Bosea_36 TRUE
## Moraxella_37 FALSE
## Bacteriovorax_stolpii_38 FALSE
## Bosea_vestrisii_39 FALSE
## Phenylobacterium_muchangponense_40 FALSE
## Rhodopseudomonas_41 TRUE
## Sphingomonas_koreensis_42 FALSE
## Achromobacter_43 FALSE
## Corynebacterium_44 FALSE
## Pseudomonas_japonica_45 TRUE
## Aquabacterium_parvum_46 FALSE
## Aquabacterium_47 FALSE
## Sphingopyxis_48 FALSE
## Thiobacillus_thioparus_49 FALSE
## Thermus_brockianus_50 TRUE
## Staphylococcus_51 FALSE
## Aquabacterium_52 FALSE
## Brevundimonas_53 FALSE
## Haemophilus_54 TRUE
## Burkholderia_Caballeronia_Paraburkholderia_55 TRUE
## Cloacibacterium_56 TRUE
## Blastococcus_57 TRUE
## Meiothermus_58 FALSE
## Corynebacterium_59 TRUE
## Alcaligenes_60 FALSE
## Methylobacterium_Methylorubrum_61 TRUE
## Corynebacterium_62 TRUE
## Neisseria_63 TRUE
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_64 TRUE
## Brevundimonas_kwangchunensis_65 TRUE
## Ralstonia_66 TRUE
## Cupriavidus_gilardii_67 FALSE
## Streptococcus_69 FALSE
## Enhydrobacter_70 TRUE
## Microbacteriaceae_71 TRUE
## Afipia_72 TRUE
## Bosea_73 FALSE
## Brevundimonas_74 TRUE
## Brachymonas_75 FALSE
## Fluviicola_76 FALSE
## Skermanella_aerolata_77 TRUE
## Cupriavidus_78 FALSE
## Acinetobacter_79 TRUE
## Pseudarthrobacter_80 TRUE
## Sphingobium_81 TRUE
## Limnohabitans_82 TRUE
## Bacillus_83 TRUE
## Sphingomonadaceae_84 TRUE
## Corynebacterium_matruchotii_85 TRUE
## Pseudomonas_86 FALSE
## Psychroglaciecola_87 TRUE
## Acinetobacter_88 TRUE
## Proteus_89 FALSE
## Streptococcus_90 TRUE
## Methylobacterium_Methylorubrum_91 TRUE
## Caulobacteraceae_92 FALSE
## Escherichia/Shigella_93 TRUE
## Halomonas_94 TRUE
## Nocardioides_95 TRUE
## Bosea_96 TRUE
## Micrococcus_97 TRUE
## Methylobacillus_98 FALSE
## Acinetobacter_100 FALSE
## Phenylobacterium_mobile_101 TRUE
## Anoxybacillus_102 TRUE
## Variovorax_paradoxus_103 TRUE
## Caulobacter_104 TRUE
## Pseudomonas_105 FALSE
## Tepidiphilus_succinatimandens_106 FALSE
## Haliangium_107 FALSE
## Dietzia_109 TRUE
## Reyranella_massiliensis_110 FALSE
## Massilia_111 TRUE
## Methylopila_oligotropha_112 TRUE
## Haemophilus_114 TRUE
## Stenotrophomonas_115 FALSE
## Sphingomonas_koreensis_116 FALSE
## Asticcacaulis_excentricus_117 FALSE
## Neisseria_118 TRUE
## Actinomyces_119 FALSE
## Sphingobium_yanoikuyae_120 FALSE
## Ralstonia_121 FALSE
## Sphingopyxis_122 FALSE
## Yonghaparkia_123 TRUE
## Brevundimonas_124 FALSE
## Brevundimonas_126 FALSE
## Anoxybacillus_127 TRUE
## Tepidimonas_128 FALSE
## Stenotrophomonas_129 TRUE
## Providencia_130 FALSE
## Blastomonas_131 FALSE
## PMMR1_132 FALSE
## Haemophilus_133 TRUE
## Mesorhizobium_134 TRUE
## Brevundimonas_135 TRUE
## Sphingobium_137 TRUE
## Vulcaniibacterium_139 FALSE
## Klenkia_140 TRUE
## Sphingomonas_141 TRUE
## Lawsonella_clevelandensis_143 FALSE
## Ellin6055_144 TRUE
## Pseudomonas_145 TRUE
## Devosia_neptuniae_146 TRUE
## Janibacter_147 TRUE
## Devosia_148 FALSE
## Brevundimonas_149 TRUE
## Candidatus_Paracaedibacter_150 FALSE
## Paucibacter_151 FALSE
## Meiothermus_152 TRUE
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_153 FALSE
## Pseudomonas_154 TRUE
## Rhodopseudomonas_155 TRUE
## Rhodopseudomonas_156 TRUE
## Frankiales_158 TRUE
## Novosphingobium_159 TRUE
## Sphingosinicella_163 FALSE
## Flavihumibacter_165 FALSE
## Duganella_166 TRUE
## Granulicatella_167 TRUE
## Sphingoaurantiacus_polygranulatus_168 TRUE
## Caulobacter_169 TRUE
## Finegoldia_171 TRUE
## Acinetobacter_lwoffii_175 TRUE
## Methylopilaceae_176 TRUE
## Halomonas_178 TRUE
## Abiotrophia_defectiva_181 TRUE
## Caulobacteraceae_182 FALSE
## Sphingomonas_184 FALSE
## Herbiconiux_185 TRUE
## Phreatobacter_oligotrophus_186 FALSE
## Alphaproteobacteria_187 FALSE
## Leifsonia_188 TRUE
## Microvirga_189 TRUE
## Porphyromonas_gingivalis_193 TRUE
## Bdellovibrio_194 FALSE
## Streptococcus_195 TRUE
## Enterobacterales_198 TRUE
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_199 TRUE
## Brachybacterium_200 FALSE
## Burkholderia_Caballeronia_Paraburkholderia_201 TRUE
## Galbitalea_203 TRUE
## Sphingobium_205 FALSE
## Paracoccus_206 FALSE
## Pyrinomonas_207 FALSE
## Brevibacillus_208 TRUE
## Luteimonas_209 TRUE
## Bosea_211 FALSE
## Micrococcaceae_212 TRUE
## Gemella_213 TRUE
## Massilia_214 TRUE
## Streptococcus_216 TRUE
## Patulibacter_minatonensis_217 TRUE
## Acidovorax_218 TRUE
## Alloprevotella_219 TRUE
## Novispirillum_220 FALSE
## Rhodococcus_221 FALSE
## Fimbriiglobus_223 TRUE
## Gammaproteobacteria_226 FALSE
## Aquabacterium_228 TRUE
## Acetobacteraceae_230 TRUE
## Bosea_231 FALSE
## Tepidiphilus_232 FALSE
## Aerococcus_233 TRUE
## Geodermatophilus_234 TRUE
## Limnobacter_thiooxidans_236 TRUE
## Methylobacterium_Methylorubrum_240 TRUE
## Hansschlegelia_242 TRUE
## Roseomonas_cervicalis_243 FALSE
## Legionella_lytica_244 FALSE
## Klebsiella_245 TRUE
## Methylobacterium_Methylorubrum_246 TRUE
## Prevotella_histicola_247 TRUE
## Prevotella_248 TRUE
## Porphyromonas_254 TRUE
## Belnapia_rosea_259 TRUE
## Methylophilus_263 TRUE
## Staphylococcus_264 TRUE
## Methylobacterium_Methylorubrum_265 FALSE
## Turicella_otitidis_268 TRUE
## Quadrisphaera_271 TRUE
## Caulobacter_272 FALSE
## Stenotrophomonas_277 TRUE
## Frankiales_280 TRUE
## Stenotrophomonas_282 FALSE
## Phenylobacterium_286 TRUE
## Devosia_288 TRUE
## Pseudoxanthomonas_mexicana_289 TRUE
## Clostridium_sensu_stricto_1_293 TRUE
## Mesorhizobium_294 TRUE
## KD4_96_297 TRUE
## Kocuria_299 TRUE
## Streptococcus_303 TRUE
## Novosphingobium_311 FALSE
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_314 TRUE
## Vulcaniibacterium_thermophilum_321 FALSE
## Kocuria_330 TRUE
## DSSD61_338 TRUE
## Dietzia_timorensis_339 TRUE
## Ruminiclostridium_341 TRUE
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_343 TRUE
## Paenarthrobacter_344 TRUE
## Candidatus_Paracaedibacter_347 TRUE
## Veillonella_348 TRUE
## Cnuella_349 TRUE
## UCG_005_354 TRUE
## Cellulomonas_360 TRUE
## Acetitomaculum_362 TRUE
## Thermomonas_hydrothermalis_363 TRUE
## Ensifer_365 TRUE
## Sphingomonas_367 TRUE
## Burkholderia_Caballeronia_Paraburkholderia_374 TRUE
## Pedobacter_381 TRUE
## Hydrocarboniphaga_389 FALSE
## Pseudoxanthomonas_447 TRUE6.5.1 Inspect the number of times these taxa were observed in positive and negative samples
# Make phyloseq object of presence-absence in negative controls and true samples
ps.pa <- transform_sample_counts(ps_filtered, function(abund) 1*(abund>0))
ps.pa.neg <- prune_samples(sample_data(ps.pa)$Sample_type == "blank", ps.pa)
ps.pa.pos <- prune_samples(sample_data(ps.pa)$is.neg == "FALSE", ps.pa)
# Make data.frame of prevalence in positive and negative samples
df.pa <- data.frame(pa.pos=taxa_sums(ps.pa.pos), pa.neg=taxa_sums(ps.pa.neg),
contaminant=notcontamdf$not.contaminant)
ggplot(data=df.pa, aes(x=pa.neg, y=pa.pos, color=contaminant)) + geom_point() +
xlab("Prevalence (Negative Controls)") + ylab("Prevalence (STGG Samples)")
6.5.2 Decontam MiSeq RunNo.514
sample_data(ps_filtered514)$is.neg <- sample_data(ps_filtered514)$Sample_type == "blank"
ps_filtered514 = prune_taxa(taxa_sums(ps_filtered514)>0.1, ps_filtered514)
#Identify true (STGG)
notcontamdf_514 <- isNotContaminant(ps_filtered514, neg ="is.neg", threshold = 0.1, detailed=TRUE)
table(notcontamdf_514$not.contaminant)##
## FALSE TRUE
## 147 38head(which(notcontamdf_514$not.contaminant))## [1] 2 3 5 6 8 9select(notcontamdf_514, not.contaminant)## not.contaminant
## Tepidimonas_1 FALSE
## Thermus_3 TRUE
## Thermus_5 TRUE
## Caulobacter_6 FALSE
## Sphingomonas_8 TRUE
## Thermus_thermophilus_10 TRUE
## Sphingobium_11 FALSE
## Pseudomonas_12 TRUE
## Methylobacterium_Methylorubrum_13 TRUE
## Staphylococcus_17 FALSE
## Nitriliruptoraceae_18 FALSE
## Paucibacter_19 FALSE
## Delftia_21 TRUE
## Methylobacterium_Methylorubrum_22 TRUE
## Bradyrhizobium_24 TRUE
## Methylobacterium_Methylorubrum_26 TRUE
## Noviherbaspirillum_suwonense_27 FALSE
## Xanthobacter_autotrophicus_29 FALSE
## Dolosigranulum_pigrum_30 FALSE
## Lactococcus_31 TRUE
## Methylobacterium_Methylorubrum_32 FALSE
## Pseudomonas_33 TRUE
## Streptococcus_34 TRUE
## Corynebacterium_35 TRUE
## Bosea_36 FALSE
## Moraxella_37 FALSE
## Rhodopseudomonas_41 FALSE
## Sphingomonas_koreensis_42 FALSE
## Corynebacterium_44 FALSE
## Pseudomonas_japonica_45 TRUE
## Aquabacterium_parvum_46 FALSE
## Thermus_brockianus_50 TRUE
## Staphylococcus_51 FALSE
## Haemophilus_54 FALSE
## Burkholderia_Caballeronia_Paraburkholderia_55 FALSE
## Cloacibacterium_56 FALSE
## Blastococcus_57 TRUE
## Meiothermus_58 FALSE
## Corynebacterium_59 FALSE
## Methylobacterium_Methylorubrum_61 FALSE
## Corynebacterium_62 FALSE
## Neisseria_63 TRUE
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_64 FALSE
## Brevundimonas_kwangchunensis_65 TRUE
## Ralstonia_66 FALSE
## Streptococcus_69 FALSE
## Enhydrobacter_70 TRUE
## Microbacteriaceae_71 FALSE
## Afipia_72 FALSE
## Brevundimonas_74 FALSE
## Skermanella_aerolata_77 TRUE
## Acinetobacter_79 TRUE
## Pseudarthrobacter_80 TRUE
## Sphingobium_81 TRUE
## Limnohabitans_82 FALSE
## Bacillus_83 FALSE
## Sphingomonadaceae_84 FALSE
## Corynebacterium_matruchotii_85 FALSE
## Psychroglaciecola_87 FALSE
## Acinetobacter_88 TRUE
## Streptococcus_90 FALSE
## Methylobacterium_Methylorubrum_91 TRUE
## Caulobacteraceae_92 FALSE
## Escherichia/Shigella_93 FALSE
## Halomonas_94 FALSE
## Nocardioides_95 FALSE
## Bosea_96 TRUE
## Micrococcus_97 FALSE
## Phenylobacterium_mobile_101 FALSE
## Anoxybacillus_102 TRUE
## Variovorax_paradoxus_103 FALSE
## Caulobacter_104 FALSE
## Dietzia_109 FALSE
## Massilia_111 FALSE
## Methylopila_oligotropha_112 FALSE
## Haemophilus_114 FALSE
## Stenotrophomonas_115 FALSE
## Neisseria_118 FALSE
## Actinomyces_119 FALSE
## Ralstonia_121 FALSE
## Yonghaparkia_123 TRUE
## Anoxybacillus_127 FALSE
## Stenotrophomonas_129 FALSE
## Haemophilus_133 FALSE
## Mesorhizobium_134 FALSE
## Brevundimonas_135 FALSE
## Sphingobium_137 FALSE
## Klenkia_140 TRUE
## Sphingomonas_141 FALSE
## Lawsonella_clevelandensis_143 FALSE
## Ellin6055_144 FALSE
## Pseudomonas_145 TRUE
## Devosia_neptuniae_146 TRUE
## Janibacter_147 FALSE
## Brevundimonas_149 FALSE
## Paucibacter_151 FALSE
## Meiothermus_152 TRUE
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_153 FALSE
## Pseudomonas_154 FALSE
## Rhodopseudomonas_155 FALSE
## Rhodopseudomonas_156 FALSE
## Frankiales_158 FALSE
## Novosphingobium_159 FALSE
## Duganella_166 FALSE
## Granulicatella_167 FALSE
## Sphingoaurantiacus_polygranulatus_168 TRUE
## Caulobacter_169 TRUE
## Finegoldia_171 FALSE
## Acinetobacter_lwoffii_175 FALSE
## Methylopilaceae_176 TRUE
## Halomonas_178 FALSE
## Abiotrophia_defectiva_181 FALSE
## Sphingomonas_184 FALSE
## Herbiconiux_185 FALSE
## Leifsonia_188 FALSE
## Microvirga_189 FALSE
## Porphyromonas_gingivalis_193 FALSE
## Streptococcus_195 FALSE
## Enterobacterales_198 FALSE
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_199 FALSE
## Brachybacterium_200 FALSE
## Burkholderia_Caballeronia_Paraburkholderia_201 FALSE
## Galbitalea_203 FALSE
## Sphingobium_205 FALSE
## Paracoccus_206 FALSE
## Brevibacillus_208 TRUE
## Luteimonas_209 FALSE
## Micrococcaceae_212 FALSE
## Gemella_213 FALSE
## Massilia_214 FALSE
## Streptococcus_216 FALSE
## Patulibacter_minatonensis_217 TRUE
## Acidovorax_218 FALSE
## Alloprevotella_219 FALSE
## Rhodococcus_221 FALSE
## Fimbriiglobus_223 FALSE
## Aquabacterium_228 FALSE
## Acetobacteraceae_230 FALSE
## Aerococcus_233 FALSE
## Geodermatophilus_234 FALSE
## Limnobacter_thiooxidans_236 FALSE
## Methylobacterium_Methylorubrum_240 FALSE
## Hansschlegelia_242 FALSE
## Klebsiella_245 FALSE
## Methylobacterium_Methylorubrum_246 FALSE
## Prevotella_histicola_247 FALSE
## Prevotella_248 FALSE
## Porphyromonas_254 FALSE
## Belnapia_rosea_259 FALSE
## Methylophilus_263 FALSE
## Staphylococcus_264 FALSE
## Turicella_otitidis_268 FALSE
## Quadrisphaera_271 FALSE
## Stenotrophomonas_277 FALSE
## Frankiales_280 FALSE
## Stenotrophomonas_282 FALSE
## Phenylobacterium_286 FALSE
## Devosia_288 FALSE
## Pseudoxanthomonas_mexicana_289 FALSE
## Clostridium_sensu_stricto_1_293 FALSE
## Mesorhizobium_294 FALSE
## KD4_96_297 FALSE
## Kocuria_299 FALSE
## Streptococcus_303 FALSE
## Novosphingobium_311 FALSE
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_314 FALSE
## Kocuria_330 FALSE
## DSSD61_338 FALSE
## Dietzia_timorensis_339 FALSE
## Ruminiclostridium_341 FALSE
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_343 FALSE
## Paenarthrobacter_344 FALSE
## Candidatus_Paracaedibacter_347 FALSE
## Veillonella_348 FALSE
## Cnuella_349 FALSE
## UCG_005_354 FALSE
## Cellulomonas_360 FALSE
## Acetitomaculum_362 FALSE
## Thermomonas_hydrothermalis_363 FALSE
## Ensifer_365 FALSE
## Sphingomonas_367 FALSE
## Burkholderia_Caballeronia_Paraburkholderia_374 FALSE
## Pedobacter_381 FALSE
## Hydrocarboniphaga_389 FALSE
## Pseudoxanthomonas_447 FALSEnotcontamdf_514_T <- notcontamdf_514 %>%
select(not.contaminant) %>%
filter(not.contaminant==FALSE)
library(data.table)
notcontamdf_514_T <- setDT(notcontamdf_514_T, keep.rownames = TRUE)[]
notcontamdf_514_T10 <- slice_head(notcontamdf_514_T, n=10)# Make phyloseq object of presence-absence in negative controls and true samples
ps.pa514 <- transform_sample_counts(ps_filtered514, function(abund) 1*(abund>0))
ps.pa.neg514 <- prune_samples(sample_data(ps.pa514)$Sample_type == "blank", ps.pa514)
ps.pa.pos514 <- prune_samples(sample_data(ps.pa514)$is.neg == "FALSE", ps.pa514)
# Make data.frame of prevalence in positive and negative samples
df.pa514 <- data.frame(pa.pos514=taxa_sums(ps.pa.pos514), pa.neg514=taxa_sums(ps.pa.neg514),
NotContaminant514=notcontamdf_514$not.contaminant)
ggplot(data=df.pa514, aes(x=pa.neg514, y=pa.pos514, color=NotContaminant514)) +
geom_point() +
xlab("Prevalence (Negative Controls)") + ylab("Prevalence (STGG Samples)")
### Use Boxplot to compare relative abundace of ASVs indicated by isNotContam
OTU8<- as(otu_table(ps_filtered514), "matrix")
OTU8 <- OTU8[rowSums(t(t(OTU8)/colSums(OTU8))>=0.001)>1, ]
otu_table_ord8<-OTU8[order(-rowSums(OTU8)), ]
otu_table_ord_RA8 <- t(t(otu_table_ord8)/colSums(otu_table_ord8))
otu_table_ord_RA8 <- t(otu_table_ord_RA8)
vector1 <-c(notcontamdf_514_T$rn) # create a vector something line that
otutable.contam <- otu_table_ord_RA8[,colnames(otu_table_ord_RA8)%in%vector1] # to subset for only otus of interest if ASVs are columns
otutable.contam.t <- t(otutable.contam)boxplot.dfT_NC514 <- cbind(data.frame(Sample.ID=rownames(metadata514)), t(otutable.contam.t[1:10,]) %>%
data.frame(check.names = F) %>%
mutate(Residuals = 1 - rowSums(.))) %>%
gather(key=ASV, value=RA, -Sample.ID) %>%
mutate(ASV = factor(ASV, levels = rev(unique(ASV))))## Warning: attributes are not identical across measure variables;
## they will be droppedSample_group6<-metadata514%>%
select(Sample, Sample_type) %>%
deframe()
boxplot.dfT_NC514$Sample_group6<-Sample_group6[match(boxplot.dfT_NC514$Sample.ID, names(Sample_group6))]
boxplot.dfT_NC514 <- boxplot.dfT_NC514 %>%
mutate(RA_percentage=RA*100)%>%
mutate(log10_RA=log10(RA_percentage))
ggplot(data = boxplot.dfT_NC514, aes(x=ASV, y=log10_RA, fill=Sample_group6))+
geom_boxplot()+
ylab("Log10(Relative abundance)")+
xlab("Sample")+
theme_bw() +
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5), legend.position = "bottom") +
ggsave("Relative_abundances_Boxplot_Notcontam514.tiff", units="in", width=8, height=10, dpi=300, compression = 'lzw')## Warning: Removed 156 rows containing non-finite values (stat_boxplot).## Warning: Removed 156 rows containing non-finite values (stat_boxplot).
## Decontam RNA Protect as blanks
ps_NoBlank <- subset_samples(ps_filtered, sample_data(ps_filtered)$Sample_type !="blank")
sample_data(ps_NoBlank)$is.neg <- sample_data(ps_NoBlank)$Sample_type == "rna_protect"
#Identify true (STGG)
notcontamdfNoBlank <- isNotContaminant(ps_NoBlank, neg ="is.neg", threshold = 0.5, detailed = TRUE)
table(notcontamdfNoBlank$not.contaminant)##
## FALSE TRUE
## 124 125head(which(notcontamdfNoBlank$not.contaminant))## [1] 1 5 8 10 12 13select(notcontamdfNoBlank, not.contaminant)## not.contaminant
## Tepidimonas_1 TRUE
## Curvibacter_2 FALSE
## Thermus_3 FALSE
## Schlegelella_4 FALSE
## Thermus_5 TRUE
## Caulobacter_6 FALSE
## Comamonadaceae_7 FALSE
## Sphingomonas_8 TRUE
## Bosea_9 FALSE
## Thermus_thermophilus_10 TRUE
## Sphingobium_11 FALSE
## Pseudomonas_12 TRUE
## Methylobacterium_Methylorubrum_13 TRUE
## Ottowia_14 FALSE
## Azospirillum_15 FALSE
## Meiothermus_silvanus_16 FALSE
## Staphylococcus_17 FALSE
## Nitriliruptoraceae_18 TRUE
## Paucibacter_19 FALSE
## Brevundimonas_20 FALSE
## Delftia_21 TRUE
## Methylobacterium_Methylorubrum_22 TRUE
## Methyloversatilis_23 FALSE
## Bradyrhizobium_24 TRUE
## Comamonas_25 FALSE
## Methylobacterium_Methylorubrum_26 TRUE
## Noviherbaspirillum_suwonense_27 TRUE
## Brevundimonas_28 FALSE
## Xanthobacter_autotrophicus_29 FALSE
## Dolosigranulum_pigrum_30 FALSE
## Lactococcus_31 TRUE
## Methylobacterium_Methylorubrum_32 TRUE
## Pseudomonas_33 TRUE
## Streptococcus_34 TRUE
## Corynebacterium_35 FALSE
## Bosea_36 TRUE
## Moraxella_37 FALSE
## Bacteriovorax_stolpii_38 FALSE
## Bosea_vestrisii_39 FALSE
## Phenylobacterium_muchangponense_40 FALSE
## Rhodopseudomonas_41 TRUE
## Sphingomonas_koreensis_42 FALSE
## Achromobacter_43 FALSE
## Corynebacterium_44 TRUE
## Pseudomonas_japonica_45 TRUE
## Aquabacterium_parvum_46 FALSE
## Aquabacterium_47 FALSE
## Sphingopyxis_48 FALSE
## Thiobacillus_thioparus_49 FALSE
## Thermus_brockianus_50 TRUE
## Staphylococcus_51 FALSE
## Aquabacterium_52 FALSE
## Brevundimonas_53 FALSE
## Haemophilus_54 TRUE
## Burkholderia_Caballeronia_Paraburkholderia_55 TRUE
## Cloacibacterium_56 FALSE
## Blastococcus_57 TRUE
## Meiothermus_58 FALSE
## Corynebacterium_59 FALSE
## Alcaligenes_60 FALSE
## Methylobacterium_Methylorubrum_61 TRUE
## Corynebacterium_62 TRUE
## Neisseria_63 TRUE
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_64 TRUE
## Brevundimonas_kwangchunensis_65 TRUE
## Ralstonia_66 TRUE
## Cupriavidus_gilardii_67 FALSE
## Streptococcus_69 FALSE
## Enhydrobacter_70 TRUE
## Microbacteriaceae_71 FALSE
## Afipia_72 TRUE
## Bosea_73 FALSE
## Brevundimonas_74 TRUE
## Brachymonas_75 FALSE
## Fluviicola_76 FALSE
## Skermanella_aerolata_77 TRUE
## Cupriavidus_78 FALSE
## Acinetobacter_79 TRUE
## Pseudarthrobacter_80 TRUE
## Sphingobium_81 TRUE
## Limnohabitans_82 TRUE
## Bacillus_83 FALSE
## Sphingomonadaceae_84 FALSE
## Corynebacterium_matruchotii_85 TRUE
## Pseudomonas_86 FALSE
## Psychroglaciecola_87 TRUE
## Acinetobacter_88 TRUE
## Proteus_89 FALSE
## Streptococcus_90 TRUE
## Methylobacterium_Methylorubrum_91 TRUE
## Caulobacteraceae_92 FALSE
## Escherichia/Shigella_93 TRUE
## Halomonas_94 TRUE
## Nocardioides_95 TRUE
## Bosea_96 TRUE
## Micrococcus_97 TRUE
## Methylobacillus_98 FALSE
## Acinetobacter_100 FALSE
## Phenylobacterium_mobile_101 TRUE
## Anoxybacillus_102 TRUE
## Variovorax_paradoxus_103 TRUE
## Caulobacter_104 TRUE
## Pseudomonas_105 FALSE
## Tepidiphilus_succinatimandens_106 FALSE
## Haliangium_107 FALSE
## Dietzia_109 TRUE
## Reyranella_massiliensis_110 FALSE
## Massilia_111 TRUE
## Methylopila_oligotropha_112 TRUE
## Haemophilus_114 TRUE
## Stenotrophomonas_115 FALSE
## Sphingomonas_koreensis_116 FALSE
## Asticcacaulis_excentricus_117 FALSE
## Neisseria_118 TRUE
## Actinomyces_119 FALSE
## Sphingobium_yanoikuyae_120 FALSE
## Ralstonia_121 FALSE
## Sphingopyxis_122 FALSE
## Yonghaparkia_123 TRUE
## Brevundimonas_124 FALSE
## Brevundimonas_126 FALSE
## Anoxybacillus_127 TRUE
## Tepidimonas_128 FALSE
## Stenotrophomonas_129 FALSE
## Providencia_130 FALSE
## Blastomonas_131 FALSE
## PMMR1_132 FALSE
## Haemophilus_133 FALSE
## Mesorhizobium_134 FALSE
## Brevundimonas_135 TRUE
## Sphingobium_137 TRUE
## Vulcaniibacterium_139 FALSE
## Klenkia_140 TRUE
## Sphingomonas_141 FALSE
## Lawsonella_clevelandensis_143 FALSE
## Ellin6055_144 FALSE
## Pseudomonas_145 TRUE
## Devosia_neptuniae_146 TRUE
## Janibacter_147 TRUE
## Devosia_148 FALSE
## Brevundimonas_149 TRUE
## Candidatus_Paracaedibacter_150 FALSE
## Paucibacter_151 FALSE
## Meiothermus_152 TRUE
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_153 FALSE
## Pseudomonas_154 TRUE
## Rhodopseudomonas_155 TRUE
## Rhodopseudomonas_156 TRUE
## Frankiales_158 TRUE
## Novosphingobium_159 TRUE
## Sphingosinicella_163 FALSE
## Flavihumibacter_165 FALSE
## Duganella_166 TRUE
## Granulicatella_167 FALSE
## Sphingoaurantiacus_polygranulatus_168 TRUE
## Caulobacter_169 TRUE
## Finegoldia_171 TRUE
## Acinetobacter_lwoffii_175 TRUE
## Methylopilaceae_176 TRUE
## Halomonas_178 FALSE
## Abiotrophia_defectiva_181 TRUE
## Caulobacteraceae_182 FALSE
## Sphingomonas_184 FALSE
## Herbiconiux_185 TRUE
## Phreatobacter_oligotrophus_186 FALSE
## Alphaproteobacteria_187 FALSE
## Leifsonia_188 TRUE
## Microvirga_189 TRUE
## Porphyromonas_gingivalis_193 TRUE
## Bdellovibrio_194 FALSE
## Streptococcus_195 FALSE
## Enterobacterales_198 TRUE
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_199 TRUE
## Brachybacterium_200 FALSE
## Burkholderia_Caballeronia_Paraburkholderia_201 TRUE
## Galbitalea_203 TRUE
## Sphingobium_205 FALSE
## Paracoccus_206 FALSE
## Pyrinomonas_207 FALSE
## Brevibacillus_208 TRUE
## Luteimonas_209 TRUE
## Bosea_211 FALSE
## Micrococcaceae_212 FALSE
## Gemella_213 FALSE
## Massilia_214 TRUE
## Streptococcus_216 FALSE
## Patulibacter_minatonensis_217 TRUE
## Acidovorax_218 TRUE
## Alloprevotella_219 TRUE
## Novispirillum_220 FALSE
## Rhodococcus_221 FALSE
## Fimbriiglobus_223 TRUE
## Gammaproteobacteria_226 FALSE
## Aquabacterium_228 TRUE
## Acetobacteraceae_230 TRUE
## Bosea_231 FALSE
## Tepidiphilus_232 FALSE
## Aerococcus_233 TRUE
## Geodermatophilus_234 FALSE
## Limnobacter_thiooxidans_236 FALSE
## Methylobacterium_Methylorubrum_240 TRUE
## Hansschlegelia_242 TRUE
## Roseomonas_cervicalis_243 FALSE
## Legionella_lytica_244 FALSE
## Klebsiella_245 FALSE
## Methylobacterium_Methylorubrum_246 TRUE
## Prevotella_histicola_247 TRUE
## Prevotella_248 TRUE
## Porphyromonas_254 TRUE
## Belnapia_rosea_259 FALSE
## Methylophilus_263 TRUE
## Staphylococcus_264 FALSE
## Methylobacterium_Methylorubrum_265 FALSE
## Turicella_otitidis_268 TRUE
## Quadrisphaera_271 TRUE
## Caulobacter_272 FALSE
## Stenotrophomonas_277 FALSE
## Frankiales_280 FALSE
## Stenotrophomonas_282 FALSE
## Phenylobacterium_286 TRUE
## Devosia_288 TRUE
## Pseudoxanthomonas_mexicana_289 TRUE
## Clostridium_sensu_stricto_1_293 FALSE
## Mesorhizobium_294 FALSE
## KD4_96_297 TRUE
## Kocuria_299 TRUE
## Streptococcus_303 TRUE
## Novosphingobium_311 FALSE
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_314 TRUE
## Vulcaniibacterium_thermophilum_321 FALSE
## Kocuria_330 TRUE
## DSSD61_338 FALSE
## Dietzia_timorensis_339 FALSE
## Ruminiclostridium_341 FALSE
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_343 FALSE
## Paenarthrobacter_344 FALSE
## Candidatus_Paracaedibacter_347 TRUE
## Veillonella_348 TRUE
## Cnuella_349 TRUE
## UCG_005_354 TRUE
## Cellulomonas_360 FALSE
## Acetitomaculum_362 TRUE
## Thermomonas_hydrothermalis_363 TRUE
## Ensifer_365 TRUE
## Sphingomonas_367 TRUE
## Burkholderia_Caballeronia_Paraburkholderia_374 TRUE
## Pedobacter_381 TRUE
## Hydrocarboniphaga_389 FALSE
## Pseudoxanthomonas_447 TRUE# Make phyloseq object of presence-absence in negative controls and true samples
ps.pa.NoBlanks <- transform_sample_counts(ps_NoBlank, function(abund) 1*(abund>0))
ps.pa.neg.NoBlanks <- prune_samples(sample_data(ps.pa.NoBlanks)$Sample_type == "rna_protect", ps.pa.NoBlanks)
ps.pa.pos.NoBlanks <- prune_samples(sample_data(ps.pa.NoBlanks)$is.neg == "FALSE", ps.pa.NoBlanks)
# Make data.frame of prevalence in positive and negative samples
df.pa.NoBlanks <- data.frame(pa.pos.NoBlanks=taxa_sums(ps.pa.pos.NoBlanks), pa.neg.NoBlanks=taxa_sums(ps.pa.neg.NoBlanks),
contaminant.NoBlanks=notcontamdfNoBlank$not.contaminant)
ggplot(data=df.pa.NoBlanks, aes(x=pa.neg.NoBlanks, y=pa.pos.NoBlanks, color=contaminant.NoBlanks)) + geom_point() +
xlab("Prevalence (Negative Controls)") + ylab("Prevalence (True Samples)")
7 Metagenomeseq
7.1 Differential Abundance testing for all samples
# Check zero-inflated distribution of the first ASV in your dataset
dat <- data.frame(strep=otu_table(ps_filtered514)[1,])%>%t
hist(dat)
print(wilcox.test(dat))## Warning in wilcox.test.default(dat): cannot compute exact p-value with ties##
## Wilcoxon signed rank test with continuity correction
##
## data: dat
## V = 561, p-value = 5.639e-07
## alternative hypothesis: true location is not equal to 07.2 Updated with fitFeatureModel
# raw sequence count phyloseq
metaSeq <- phyloseq_to_metagenomeSeq(ps_filtered)
# internal normalisation
metaSeq <- cumNorm(metaSeq, p=0.5)
# formula
mod <- model.matrix(~ Sample_type+Run_No, pData(metaSeq))
# NOT fitZig; gives higher # false-positives
fit <- fitFeatureModel(metaSeq, mod) ## Error in fitFeatureModel(metaSeq, mod): Can't analyze currently.VP.df <- MRcoefs(fit, number = Inf, alpha = 1, adjustMethod = "BH")## Error in grep("fitFeatureModel", obj@call): object 'fit' not foundfitFeatureModel will not run with all 3 sample types. Is it possible to fix this? For when run with a phyloseq object contianing only two samples types, it will not let me correct for Miseq Run, how might I fix this?
7.2.1 STGG vs Blank
ps_SB <- subset_samples(ps_filtered, sample_data(ps_filtered)$Sample_type!="rna_protect")
ps_SB <- prune_taxa(taxa_sums(ps_SB)>0, ps_SB)
metaSeq_SB <- phyloseq_to_metagenomeSeq(ps_SB) # raw sequence count phyloseq
metaSeq_norm_SB <- cumNorm(metaSeq_SB, p=0.5) # internal normalisation
mod3_SB <- model.matrix(~ Sample_type, pData(metaSeq_norm_SB)) # formula
fit_SB <- fitFeatureModel(metaSeq_norm_SB, mod3_SB) # NOT fitZig; gives higher # false-positives## Warning: Partial NA coefficients for 142 probe(s)## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredVP.df_SB <- MRcoefs(fit_SB, number = Inf, alpha = 1, adjustMethod = "BH") # extract all output (not only significant)
VP.df_SB## logFC se
## Curvibacter_2 -3.963920483 0.54024188
## Thermus_thermophilus_10 3.552298875 0.70664167
## Sphingobium_11 -3.424260724 0.68324742
## Schlegelella_4 -3.241970855 0.44356943
## Tepidimonas_1 2.911508392 0.33334908
## Caulobacter_6 -2.838307400 0.39009662
## Comamonadaceae_7 -2.811768245 0.38415314
## Bosea_9 -2.677007920 0.37494684
## Lactococcus_31 2.595015822 0.24423757
## Thermus_5 2.443574805 0.37124598
## Meiothermus_silvanus_16 -2.428599998 0.35355026
## Acinetobacter_79 2.368166286 1.01461078
## Azospirillum_15 -2.331226207 0.31962883
## Brevundimonas_20 -2.256837716 0.34467707
## Ottowia_14 -2.056958186 0.31960812
## Corynebacterium_35 2.031525174 1.04295366
## Methyloversatilis_23 -1.990723579 0.29957364
## Blastococcus_57 1.855546366 0.97286818
## Methylobacterium_Methylorubrum_26 1.832230560 0.75895135
## Methylobacterium_Methylorubrum_22 1.709138025 0.55980447
## Streptococcus_34 1.704724233 0.79762261
## Methylobacterium_Methylorubrum_13 1.698238092 0.43251168
## Pseudomonas_japonica_45 1.668486465 0.70785140
## Thermus_3 -1.636336378 0.33784539
## Cloacibacterium_56 1.635541147 0.83302063
## Xanthobacter_autotrophicus_29 -1.501782847 0.24157973
## Caulobacter_104 1.494881366 0.93098494
## Comamonas_25 -1.413191603 0.30189316
## Pseudomonas_33 1.402267854 0.54002826
## Bosea_36 -1.379869308 0.50074680
## Bacteriovorax_stolpii_38 -1.354585647 0.25673482
## Phenylobacterium_muchangponense_40 -1.290831223 0.25388296
## Brevundimonas_28 -1.277026780 0.26487590
## Achromobacter_43 -1.255918897 0.22431716
## Enhydrobacter_70 1.226268900 0.81295600
## Aquabacterium_parvum_46 -1.161185588 0.28904262
## Corynebacterium_62 1.148695832 0.81361395
## Bosea_vestrisii_39 -1.147818216 0.24290929
## Ralstonia_66 1.138437160 0.90870319
## Frankiales_158 1.105763874 1.05104949
## Thiobacillus_thioparus_49 -1.090671274 0.21386584
## Brevundimonas_kwangchunensis_65 1.057245846 0.28958925
## Corynebacterium_44 -1.020774152 1.01626585
## Massilia_111 0.985823709 0.98785481
## Noviherbaspirillum_suwonense_27 0.926612355 0.46608926
## Aquabacterium_52 -0.926061717 0.20180008
## Sphingomonas_koreensis_42 -0.925171164 0.48803579
## Bradyrhizobium_24 0.925035181 0.47757219
## Neisseria_118 0.914763998 1.06663054
## Anoxybacillus_102 0.870518085 0.20941379
## Pseudarthrobacter_80 0.840088027 0.24241209
## Psychroglaciecola_87 0.794033897 0.93399428
## Thermus_brockianus_50 0.792449755 0.29877557
## Cupriavidus_gilardii_67 -0.761755269 0.22494231
## Alcaligenes_60 -0.732521082 0.20214971
## Sphingopyxis_48 -0.732216989 0.22818616
## Rhodopseudomonas_41 0.720576901 0.78124336
## Haemophilus_114 0.718428433 0.97349974
## Nitriliruptoraceae_18 0.712251832 0.21533012
## Bosea_73 -0.702680948 0.22255246
## Methylobacterium_Methylorubrum_32 0.702046287 0.79198550
## Phenylobacterium_mobile_101 0.701745875 0.96932506
## Micrococcus_97 0.700658864 1.02329101
## Staphylococcus_17 -0.699978904 0.42735824
## Sphingomonadaceae_84 -0.687145652 0.81099567
## Fluviicola_76 -0.685869437 0.20114040
## Neisseria_63 0.676585111 0.26303411
## Halomonas_94 0.670294593 0.87279580
## Bosea_96 0.633664419 0.23359246
## Brevundimonas_53 -0.623822194 0.24837562
## Aquabacterium_47 -0.601037826 0.20861306
## Methylobacillus_98 -0.584241762 0.17422687
## Sphingobium_81 0.556358842 0.23812505
## Brevundimonas_149 -0.549232639 1.08725802
## Haliangium_107 -0.544385887 0.15916637
## Methylobacterium_Methylorubrum_91 0.537761686 0.22934077
## Brachymonas_75 -0.529270586 0.21414760
## Methylopilaceae_176 0.519213558 0.18543056
## Skermanella_aerolata_77 0.515257019 0.23089355
## Meiothermus_58 -0.511884937 0.26523616
## Meiothermus_152 0.498360896 0.19867986
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_64 0.491078027 0.76202464
## Streptococcus_69 -0.466092702 0.29454756
## Klenkia_140 0.459072019 0.19637345
## Limnohabitans_82 0.458669941 0.28419442
## Pseudomonas_145 0.446086871 0.19151791
## Brevundimonas_126 -0.425752213 0.16056151
## PMMR1_132 -0.422983646 0.19506689
## Variovorax_paradoxus_103 0.418191171 0.21746017
## Cupriavidus_78 -0.414011411 0.21089969
## Proteus_89 -0.409127713 0.17813720
## Sphingoaurantiacus_polygranulatus_168 0.407185648 0.17836730
## Yonghaparkia_123 0.404184353 0.18766425
## Caulobacter_169 0.396750701 0.17491073
## Tepidimonas_128 -0.395409252 0.15957817
## Acinetobacter_88 0.391927337 0.20498000
## Haemophilus_54 0.391008813 0.25968088
## Asticcacaulis_excentricus_117 -0.388546328 0.14928009
## Tepidiphilus_succinatimandens_106 -0.385662030 0.17261428
## Duganella_166 0.384434240 0.21990728
## Reyranella_massiliensis_110 -0.381838606 0.14547951
## Sphingobium_yanoikuyae_120 -0.371584869 0.16787301
## Acinetobacter_100 -0.360139807 0.16345310
## Acinetobacter_lwoffii_175 0.357524508 0.19383129
## Stenotrophomonas_129 0.353858446 0.17835345
## Devosia_neptuniae_146 0.348560338 0.16532515
## Enterobacterales_198 0.342190601 0.18791464
## Sphingopyxis_122 -0.341739021 0.15151079
## Pseudomonas_86 -0.340678224 0.14682081
## Porphyromonas_gingivalis_193 0.338995717 0.17287530
## Devosia_288 0.337951981 0.15723643
## Brevibacillus_208 0.336762461 0.15594845
## Leifsonia_188 0.336598815 0.17206004
## Escherichia/Shigella_93 -0.330964297 0.78060795
## Pseudomonas_105 -0.329078955 0.15992852
## Actinomyces_119 -0.316059715 1.07016058
## Microvirga_189 0.315134664 0.18396903
## Sphingomonas_koreensis_116 -0.312532184 0.15832369
## Delftia_21 0.306994634 0.53901147
## Providencia_130 -0.305328296 0.12898551
## Brevundimonas_74 -0.305052881 0.79466623
## Janibacter_147 0.294251473 0.16479329
## Phreatobacter_oligotrophus_186 -0.294121317 0.14142600
## Patulibacter_minatonensis_217 0.293922647 0.14279371
## Galbitalea_203 0.292557670 0.17447576
## Methylobacterium_Methylorubrum_240 0.291362970 0.16907351
## Luteimonas_209 0.287593575 0.15663130
## Rhodopseudomonas_155 0.282440856 0.21383797
## Paucibacter_19 -0.281179594 0.24297623
## Devosia_148 -0.277501084 0.17084116
## Dolosigranulum_pigrum_30 -0.274721356 0.67898716
## Anoxybacillus_127 0.274640210 0.22655783
## Herbiconiux_185 0.271823429 0.15316667
## Tepidiphilus_232 -0.268334872 0.14711061
## Acidovorax_218 0.267629907 0.16666676
## Pedobacter_381 0.256897376 0.12720407
## Turicella_otitidis_268 0.253970052 0.15299221
## Novosphingobium_159 0.247131758 0.21917341
## Methylopila_oligotropha_112 0.246701340 0.17808762
## Legionella_lytica_244 -0.246312823 0.12932934
## Cnuella_349 0.245920444 0.12898330
## Aerococcus_233 0.245857698 0.14035048
## Massilia_214 0.244573375 0.16104351
## Nocardioides_95 0.244490540 1.11573925
## Sphingomonas_184 -0.241800476 0.18216838
## Methylophilus_263 0.241053997 0.13451726
## Paucibacter_151 -0.239552697 0.22741507
## Bdellovibrio_194 -0.239464020 0.14837756
## Afipia_72 0.239196103 0.77298698
## Streptococcus_303 0.237268049 0.14596694
## Caulobacteraceae_182 -0.236737612 0.13512250
## Methylobacterium_Methylorubrum_246 0.236637681 0.15322072
## Prevotella_histicola_247 0.236433090 0.15137060
## Burkholderia_Caballeronia_Paraburkholderia_201 0.230958600 0.15638759
## Acetitomaculum_362 0.230206767 0.12288702
## Candidatus_Paracaedibacter_347 0.227320058 0.12325681
## Hansschlegelia_242 0.227220773 0.14853328
## Rhodopseudomonas_156 0.225903687 0.18700430
## Aquabacterium_228 0.222827118 0.15034607
## Sphingosinicella_163 -0.218314518 0.17353684
## Methylobacterium_Methylorubrum_61 0.217374812 0.60075262
## Sphingobium_205 -0.216686673 0.13865897
## Corynebacterium_matruchotii_85 0.215052784 0.17259189
## Bosea_211 -0.212278266 0.11380117
## Sphingomonas_367 0.211822824 0.13412553
## Blastomonas_131 -0.211024256 0.13244995
## Thermomonas_hydrothermalis_363 0.210861478 0.13113563
## Mesorhizobium_134 0.207100945 0.19758504
## Prevotella_248 0.206603733 0.14057789
## Pseudoxanthomonas_mexicana_289 0.203667405 0.13384922
## Burkholderia_Caballeronia_Paraburkholderia_374 0.200953498 0.12702186
## Ensifer_365 0.199620037 0.12778726
## Quadrisphaera_271 0.199498862 0.13647411
## Kocuria_299 0.198946912 0.13275762
## Bosea_231 -0.197138082 0.10720533
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_199 0.196232907 0.19031143
## Caulobacter_272 -0.196093219 0.14171211
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_314 0.195954813 0.12966231
## Abiotrophia_defectiva_181 0.195459848 0.14454876
## Granulicatella_167 0.193407338 0.13733518
## Klebsiella_245 0.193162356 0.12451368
## Pyrinomonas_207 -0.192348109 0.10719518
## Sphingomonas_141 0.191559433 0.19112018
## Fimbriiglobus_223 0.191123005 0.14304231
## KD4_96_297 0.188489430 0.13033345
## Gemella_213 0.187364851 0.13871621
## Brevundimonas_124 -0.185225092 0.12195254
## Brevundimonas_135 0.184342138 0.21093018
## Novosphingobium_311 -0.182964476 0.16936096
## Roseomonas_cervicalis_243 -0.182650078 0.12126265
## Kocuria_330 0.182120841 0.12579987
## Novispirillum_220 -0.180106930 0.11948014
## Veillonella_348 0.179239238 0.12563011
## Vulcaniibacterium_thermophilum_321 -0.178796961 0.12933383
## Phenylobacterium_286 0.178705091 0.12781783
## Methylobacterium_Methylorubrum_265 -0.176590912 0.11362628
## Dietzia_109 0.172830010 0.20302292
## Burkholderia_Caballeronia_Paraburkholderia_55 0.166877054 0.70303463
## UCG_005_354 0.164785632 0.11800280
## Stenotrophomonas_115 -0.162461763 0.15390972
## Vulcaniibacterium_139 -0.161916807 0.10803701
## Porphyromonas_254 0.157013407 0.12091507
## Microbacteriaceae_71 0.152522675 0.15931139
## Alloprevotella_219 0.148825655 0.13753308
## Flavihumibacter_165 -0.144906207 0.11479531
## Lawsonella_clevelandensis_143 -0.141141860 0.18869082
## Pseudoxanthomonas_447 0.137459032 0.10264615
## Caulobacteraceae_92 -0.137383983 0.21157872
## Sphingobium_137 0.136830606 1.01632016
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_153 -0.132013942 0.23442312
## Alphaproteobacteria_187 -0.130433734 0.10777130
## Bacillus_83 0.129136681 0.23721853
## Candidatus_Paracaedibacter_150 -0.126986570 0.10379040
## Pseudomonas_12 -0.121512220 0.25066714
## Mesorhizobium_294 0.099852139 0.10267611
## Acetobacteraceae_230 0.097216459 0.16088468
## Moraxella_37 -0.095923676 0.87415630
## Staphylococcus_51 -0.087932701 0.29118450
## Streptococcus_216 0.083795559 0.10754634
## Sphingomonas_8 -0.073261151 0.49395781
## Haemophilus_133 -0.069558928 0.22019559
## Hydrocarboniphaga_389 -0.068674994 0.17672423
## Stenotrophomonas_282 -0.060029871 0.17647219
## Ellin6055_144 -0.059040401 0.15895183
## Finegoldia_171 0.058623633 0.20285771
## Staphylococcus_264 0.056759038 0.14144779
## Halomonas_178 0.056697053 0.15259399
## Corynebacterium_59 0.048309009 0.19486062
## Gammaproteobacteria_226 -0.048250682 0.06685013
## Rhodococcus_221 -0.047119408 0.15050347
## Limnobacter_thiooxidans_236 0.045523802 0.15773759
## Stenotrophomonas_277 0.044787650 0.12243119
## Belnapia_rosea_259 0.041120499 0.19136354
## Geodermatophilus_234 0.038544165 0.13636578
## Cellulomonas_360 0.036383980 0.14797078
## Brachybacterium_200 0.033769717 0.21296678
## Micrococcaceae_212 0.028228710 0.16265303
## Streptococcus_195 0.027343810 0.17727539
## Pseudomonas_154 0.023456657 1.05398521
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_343 -0.022614327 0.13495167
## DSSD61_338 -0.019848401 0.13904573
## Frankiales_280 -0.011570907 0.11657015
## Dietzia_timorensis_339 0.011112659 0.13640072
## Paenarthrobacter_344 -0.010638447 0.13925496
## Ruminiclostridium_341 -0.009571825 0.14426854
## Ralstonia_121 -0.007710715 0.19318124
## Paracoccus_206 -0.006791269 0.13245751
## Clostridium_sensu_stricto_1_293 0.002416800 0.14566414
## Streptococcus_90 0.001623647 1.08764735
## pvalues
## Curvibacter_2 2.178258e-13
## Thermus_thermophilus_10 4.981712e-07
## Sphingobium_11 5.393911e-07
## Schlegelella_4 2.695622e-13
## Tepidimonas_1 0.000000e+00
## Caulobacter_6 3.441691e-13
## Comamonadaceae_7 2.491340e-13
## Bosea_9 9.352519e-13
## Lactococcus_31 0.000000e+00
## Thermus_5 4.638778e-11
## Meiothermus_silvanus_16 6.457279e-12
## Acinetobacter_79 1.959238e-02
## Azospirillum_15 3.019807e-13
## Brevundimonas_20 5.843481e-11
## Ottowia_14 1.227634e-10
## Corynebacterium_35 5.143201e-02
## Methyloversatilis_23 3.028267e-11
## Blastococcus_57 5.648243e-02
## Methylobacterium_Methylorubrum_26 1.577150e-02
## Methylobacterium_Methylorubrum_22 2.264918e-03
## Streptococcus_34 3.257712e-02
## Methylobacterium_Methylorubrum_13 8.620664e-05
## Pseudomonas_japonica_45 1.841759e-02
## Thermus_3 1.276055e-06
## Cloacibacterium_56 4.960132e-02
## Xanthobacter_autotrophicus_29 5.083336e-10
## Caulobacter_104 1.083401e-01
## Comamonas_25 2.853419e-06
## Pseudomonas_33 9.413604e-03
## Bosea_36 5.858051e-03
## Bacteriovorax_stolpii_38 1.318863e-07
## Phenylobacterium_muchangponense_40 3.688766e-07
## Brevundimonas_28 1.426780e-06
## Achromobacter_43 2.157743e-08
## Enhydrobacter_70 1.314503e-01
## Aquabacterium_parvum_46 5.885610e-05
## Corynebacterium_62 1.579959e-01
## Bosea_vestrisii_39 2.297808e-06
## Ralstonia_66 2.102730e-01
## Frankiales_158 2.927734e-01
## Thiobacillus_thioparus_49 3.400270e-07
## Brevundimonas_kwangchunensis_65 2.613777e-04
## Corynebacterium_44 3.151684e-01
## Massilia_111 3.183066e-01
## Noviherbaspirillum_suwonense_27 4.680531e-02
## Aquabacterium_52 4.453621e-06
## Sphingomonas_koreensis_42 5.799926e-02
## Bradyrhizobium_24 5.275101e-02
## Neisseria_118 3.911022e-01
## Anoxybacillus_102 3.225555e-05
## Pseudarthrobacter_80 5.291741e-04
## Psychroglaciecola_87 3.952425e-01
## Thermus_brockianus_50 7.993969e-03
## Cupriavidus_gilardii_67 7.080388e-04
## Alcaligenes_60 2.904674e-04
## Sphingopyxis_48 1.332633e-03
## Rhodopseudomonas_41 3.563480e-01
## Haemophilus_114 4.605234e-01
## Nitriliruptoraceae_18 9.405865e-04
## Bosea_73 1.591980e-03
## Methylobacterium_Methylorubrum_32 3.753814e-01
## Phenylobacterium_mobile_101 4.690945e-01
## Micrococcus_97 4.935261e-01
## Staphylococcus_17 1.014382e-01
## Sphingomonadaceae_84 3.968355e-01
## Fluviicola_76 6.498578e-04
## Neisseria_63 1.010447e-02
## Halomonas_94 4.424957e-01
## Bosea_96 6.673908e-03
## Brevundimonas_53 1.201825e-02
## Aquabacterium_47 3.962734e-03
## Methylobacillus_98 7.984282e-04
## Sphingobium_81 1.946964e-02
## Brevundimonas_149 6.134507e-01
## Haliangium_107 6.256781e-04
## Methylobacterium_Methylorubrum_91 1.903652e-02
## Brachymonas_75 1.345391e-02
## Methylopilaceae_176 5.109578e-03
## Skermanella_aerolata_77 2.564285e-02
## Meiothermus_58 5.361658e-02
## Meiothermus_152 1.212925e-02
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_64 5.192911e-01
## Streptococcus_69 1.135578e-01
## Klenkia_140 1.940022e-02
## Limnohabitans_82 1.065426e-01
## Pseudomonas_145 1.984754e-02
## Brevundimonas_126 8.010057e-03
## PMMR1_132 3.012804e-02
## Variovorax_paradoxus_103 5.447124e-02
## Cupriavidus_78 4.963773e-02
## Proteus_89 2.163589e-02
## Sphingoaurantiacus_polygranulatus_168 2.243925e-02
## Yonghaparkia_123 3.125875e-02
## Caulobacter_169 2.331069e-02
## Tepidimonas_128 1.321802e-02
## Acinetobacter_88 5.587270e-02
## Haemophilus_54 1.321370e-01
## Asticcacaulis_excentricus_117 9.246567e-03
## Tepidiphilus_succinatimandens_106 2.546717e-02
## Duganella_166 8.043543e-02
## Reyranella_massiliensis_110 8.672789e-03
## Sphingobium_yanoikuyae_120 2.686403e-02
## Acinetobacter_100 2.757206e-02
## Acinetobacter_lwoffii_175 6.510828e-02
## Stenotrophomonas_129 4.725258e-02
## Devosia_neptuniae_146 3.500226e-02
## Enterobacterales_198 6.860844e-02
## Sphingopyxis_122 2.409930e-02
## Pseudomonas_86 2.032101e-02
## Porphyromonas_gingivalis_193 4.988762e-02
## Devosia_288 3.160875e-02
## Brevibacillus_208 3.081548e-02
## Leifsonia_188 5.043136e-02
## Escherichia/Shigella_93 6.715784e-01
## Pseudomonas_105 3.962253e-02
## Actinomyces_119 7.677352e-01
## Microvirga_189 8.671683e-02
## Sphingomonas_koreensis_116 4.838087e-02
## Delftia_21 5.689821e-01
## Providencia_130 1.792557e-02
## Brevundimonas_74 7.010707e-01
## Janibacter_147 7.416741e-02
## Phreatobacter_oligotrophus_186 3.755457e-02
## Patulibacter_minatonensis_217 3.955439e-02
## Galbitalea_203 9.358529e-02
## Methylobacterium_Methylorubrum_240 8.483579e-02
## Luteimonas_209 6.634018e-02
## Rhodopseudomonas_155 1.865624e-01
## Paucibacter_19 2.471781e-01
## Devosia_148 1.043071e-01
## Dolosigranulum_pigrum_30 6.857681e-01
## Anoxybacillus_127 2.254243e-01
## Herbiconiux_185 7.594901e-02
## Tepidiphilus_232 6.814682e-02
## Acidovorax_218 1.083226e-01
## Pedobacter_381 4.342814e-02
## Turicella_otitidis_268 9.691053e-02
## Novosphingobium_159 2.595046e-01
## Methylopila_oligotropha_112 1.659667e-01
## Legionella_lytica_244 5.683997e-02
## Cnuella_349 5.657151e-02
## Aerococcus_233 7.981835e-02
## Massilia_214 1.288434e-01
## Nocardioides_95 8.265498e-01
## Sphingomonas_184 1.843942e-01
## Methylophilus_263 7.313409e-02
## Paucibacter_151 2.921705e-01
## Bdellovibrio_194 1.065528e-01
## Afipia_72 7.569839e-01
## Streptococcus_303 1.040578e-01
## Caulobacteraceae_182 7.977001e-02
## Methylobacterium_Methylorubrum_246 1.224858e-01
## Prevotella_histicola_247 1.183001e-01
## Burkholderia_Caballeronia_Paraburkholderia_201 1.397200e-01
## Acetitomaculum_362 6.102415e-02
## Candidatus_Paracaedibacter_347 6.514235e-02
## Hansschlegelia_242 1.260753e-01
## Rhodopseudomonas_156 2.270421e-01
## Aquabacterium_228 1.383151e-01
## Sphingosinicella_163 2.083810e-01
## Methylobacterium_Methylorubrum_61 7.174735e-01
## Sphingobium_205 1.181159e-01
## Corynebacterium_matruchotii_85 2.127574e-01
## Bosea_211 6.213328e-02
## Sphingomonas_367 1.142700e-01
## Blastomonas_131 1.111069e-01
## Thermomonas_hydrothermalis_363 1.078429e-01
## Mesorhizobium_134 2.945644e-01
## Prevotella_248 1.416500e-01
## Pseudoxanthomonas_mexicana_289 1.281048e-01
## Burkholderia_Caballeronia_Paraburkholderia_374 1.136407e-01
## Ensifer_365 1.182579e-01
## Quadrisphaera_271 1.437940e-01
## Kocuria_299 1.339846e-01
## Bosea_231 6.593237e-02
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_199 3.024871e-01
## Caulobacter_272 1.664370e-01
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_314 1.307196e-01
## Abiotrophia_defectiva_181 1.763091e-01
## Granulicatella_167 1.590461e-01
## Klebsiella_245 1.208216e-01
## Pyrinomonas_207 7.275370e-02
## Sphingomonas_141 3.161995e-01
## Fimbriiglobus_223 1.815070e-01
## KD4_96_297 1.481185e-01
## Gemella_213 1.767895e-01
## Brevundimonas_124 1.288055e-01
## Brevundimonas_135 3.821462e-01
## Novosphingobium_311 2.799985e-01
## Roseomonas_cervicalis_243 1.320068e-01
## Kocuria_330 1.477001e-01
## Novispirillum_220 1.317027e-01
## Veillonella_348 1.536601e-01
## Vulcaniibacterium_thermophilum_321 1.668350e-01
## Phenylobacterium_286 1.620760e-01
## Methylobacterium_Methylorubrum_265 1.201515e-01
## Dietzia_109 3.946120e-01
## Burkholderia_Caballeronia_Paraburkholderia_55 8.123723e-01
## UCG_005_354 1.625774e-01
## Stenotrophomonas_115 2.911669e-01
## Vulcaniibacterium_139 1.339473e-01
## Porphyromonas_254 1.941008e-01
## Microbacteriaceae_71 3.383719e-01
## Alloprevotella_219 2.792045e-01
## Flavihumibacter_165 2.068406e-01
## Lawsonella_clevelandensis_143 4.544566e-01
## Pseudoxanthomonas_447 1.805205e-01
## Caulobacteraceae_92 5.161264e-01
## Sphingobium_137 8.929018e-01
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_153 5.733369e-01
## Alphaproteobacteria_187 2.261704e-01
## Bacillus_83 5.861810e-01
## Candidatus_Paracaedibacter_150 2.211445e-01
## Pseudomonas_12 6.278499e-01
## Mesorhizobium_294 3.308037e-01
## Acetobacteraceae_230 5.456696e-01
## Moraxella_37 9.126212e-01
## Staphylococcus_51 7.626652e-01
## Streptococcus_216 4.358868e-01
## Sphingomonas_8 8.820945e-01
## Haemophilus_133 7.520814e-01
## Hydrocarboniphaga_389 6.975722e-01
## Stenotrophomonas_282 7.337314e-01
## Ellin6055_144 7.103130e-01
## Finegoldia_171 7.725898e-01
## Staphylococcus_264 6.882199e-01
## Halomonas_178 7.102242e-01
## Corynebacterium_59 8.041996e-01
## Gammaproteobacteria_226 4.704335e-01
## Rhodococcus_221 7.542210e-01
## Limnobacter_thiooxidans_236 7.728839e-01
## Stenotrophomonas_277 7.145002e-01
## Belnapia_rosea_259 8.298597e-01
## Geodermatophilus_234 7.774430e-01
## Cellulomonas_360 8.057703e-01
## Brachybacterium_200 8.740092e-01
## Micrococcaceae_212 8.622178e-01
## Streptococcus_195 8.774167e-01
## Pseudomonas_154 9.822444e-01
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_343 8.669188e-01
## DSSD61_338 8.864898e-01
## Frankiales_280 9.209308e-01
## Dietzia_timorensis_339 9.350676e-01
## Paenarthrobacter_344 9.391045e-01
## Ruminiclostridium_341 9.471013e-01
## Ralstonia_121 9.681614e-01
## Paracoccus_206 9.591093e-01
## Clostridium_sensu_stricto_1_293 9.867624e-01
## Streptococcus_90 9.988089e-01
## adjPvalues
## Curvibacter_2 1.224259e-11
## Thermus_thermophilus_10 6.528664e-06
## Sphingobium_11 6.715420e-06
## Schlegelella_4 1.224259e-11
## Tepidimonas_1 0.000000e+00
## Caulobacter_6 1.224259e-11
## Comamonadaceae_7 1.224259e-11
## Bosea_9 2.910971e-11
## Lactococcus_31 0.000000e+00
## Thermus_5 1.050051e-09
## Meiothermus_silvanus_16 1.786514e-10
## Acinetobacter_79 8.101702e-02
## Azospirillum_15 1.224259e-11
## Brevundimonas_20 1.212522e-09
## Ottowia_14 2.351390e-09
## Corynebacterium_35 1.455292e-01
## Methyloversatilis_23 7.540384e-10
## Blastococcus_57 1.489806e-01
## Methylobacterium_Methylorubrum_26 7.272415e-02
## Methylobacterium_Methylorubrum_22 1.446063e-02
## Streptococcus_34 1.081560e-01
## Methylobacterium_Methylorubrum_13 7.666234e-04
## Pseudomonas_japonica_45 8.101702e-02
## Thermus_3 1.513037e-05
## Cloacibacterium_56 1.443380e-01
## Xanthobacter_autotrophicus_29 9.041076e-09
## Caulobacter_104 2.193227e-01
## Comamonas_25 2.960422e-05
## Pseudomonas_33 4.883307e-02
## Bosea_36 3.472987e-02
## Bacteriovorax_stolpii_38 2.052480e-06
## Phenylobacterium_muchangponense_40 5.102793e-06
## Brevundimonas_28 1.614856e-05
## Achromobacter_43 3.581853e-07
## Enhydrobacter_70 2.316817e-01
## Aquabacterium_parvum_46 5.427840e-04
## Corynebacterium_62 2.588223e-01
## Bosea_vestrisii_39 2.487627e-05
## Ralstonia_66 3.116546e-01
## Frankiales_158 4.050033e-01
## Thiobacillus_thioparus_49 4.980396e-06
## Brevundimonas_kwangchunensis_65 2.244243e-03
## Corynebacterium_44 4.279005e-01
## Massilia_111 4.284234e-01
## Noviherbaspirillum_suwonense_27 1.434865e-01
## Aquabacterium_52 4.435807e-05
## Sphingomonas_koreensis_42 1.504356e-01
## Bradyrhizobium_24 1.475843e-01
## Neisseria_118 5.093404e-01
## Anoxybacillus_102 3.089089e-04
## Pseudarthrobacter_80 4.250463e-03
## Psychroglaciecola_87 5.093404e-01
## Thermus_brockianus_50 4.432232e-02
## Cupriavidus_gilardii_67 5.185343e-03
## Alcaligenes_60 2.410879e-03
## Sphingopyxis_48 8.968257e-03
## Rhodopseudomonas_41 4.719715e-01
## Haemophilus_114 5.791431e-01
## Nitriliruptoraceae_18 6.505723e-03
## Bosea_73 1.043166e-02
## Methylobacterium_Methylorubrum_32 4.945501e-01
## Phenylobacterium_mobile_101 5.856897e-01
## Micrococcus_97 6.113831e-01
## Staphylococcus_17 2.177424e-01
## Sphingomonadaceae_84 5.093404e-01
## Fluviicola_76 4.903472e-03
## Neisseria_63 5.134723e-02
## Halomonas_94 5.621502e-01
## Bosea_96 3.864658e-02
## Brevundimonas_53 5.921930e-02
## Aquabacterium_47 2.466802e-02
## Methylobacillus_98 5.680246e-03
## Sphingobium_81 8.101702e-02
## Brevundimonas_149 7.343713e-01
## Haliangium_107 4.868558e-03
## Methylobacterium_Methylorubrum_91 8.101702e-02
## Brachymonas_75 6.320801e-02
## Methylopilaceae_176 3.103134e-02
## Skermanella_aerolata_77 9.389809e-02
## Meiothermus_58 1.483392e-01
## Meiothermus_152 5.921930e-02
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_64 6.369630e-01
## Streptococcus_69 2.240412e-01
## Klenkia_140 8.101702e-02
## Limnohabitans_82 2.193227e-01
## Pseudomonas_145 8.101702e-02
## Brevundimonas_126 4.432232e-02
## PMMR1_132 1.056603e-01
## Variovorax_paradoxus_103 1.489806e-01
## Cupriavidus_78 1.443380e-01
## Proteus_89 8.551328e-02
## Sphingoaurantiacus_polygranulatus_168 8.730271e-02
## Yonghaparkia_123 1.063592e-01
## Caulobacter_169 8.929787e-02
## Tepidimonas_128 6.320801e-02
## Acinetobacter_88 1.489806e-01
## Haemophilus_54 2.316817e-01
## Asticcacaulis_excentricus_117 4.883307e-02
## Tepidiphilus_succinatimandens_106 9.389809e-02
## Duganella_166 1.804362e-01
## Reyranella_massiliensis_110 4.694619e-02
## Sphingobium_yanoikuyae_120 9.694410e-02
## Acinetobacter_100 9.807776e-02
## Acinetobacter_lwoffii_175 1.619481e-01
## Stenotrophomonas_129 1.434865e-01
## Devosia_neptuniae_146 1.146785e-01
## Enterobacterales_198 1.642644e-01
## Sphingopyxis_122 9.092010e-02
## Pseudomonas_86 8.161181e-02
## Porphyromonas_gingivalis_193 1.443380e-01
## Devosia_288 1.063592e-01
## Brevibacillus_208 1.063592e-01
## Leifsonia_188 1.443380e-01
## Escherichia/Shigella_93 7.963001e-01
## Pseudomonas_105 1.248862e-01
## Actinomyces_119 8.515403e-01
## Microvirga_189 1.910840e-01
## Sphingomonas_koreensis_116 1.443380e-01
## Delftia_21 6.911051e-01
## Providencia_130 8.101702e-02
## Brevundimonas_74 8.157318e-01
## Janibacter_147 1.725952e-01
## Phreatobacter_oligotrophus_186 1.214427e-01
## Patulibacter_minatonensis_217 1.248862e-01
## Galbitalea_203 2.044100e-01
## Methylobacterium_Methylorubrum_240 1.886082e-01
## Luteimonas_209 1.619481e-01
## Rhodopseudomonas_155 2.832563e-01
## Paucibacter_19 3.537203e-01
## Devosia_148 2.193227e-01
## Dolosigranulum_pigrum_30 8.083337e-01
## Anoxybacillus_127 3.267832e-01
## Herbiconiux_185 1.751047e-01
## Tepidiphilus_232 1.642644e-01
## Acidovorax_218 2.193227e-01
## Pedobacter_381 1.351701e-01
## Turicella_otitidis_268 2.098324e-01
## Novosphingobium_159 3.692380e-01
## Methylopila_oligotropha_112 2.629235e-01
## Legionella_lytica_244 1.489806e-01
## Cnuella_349 1.489806e-01
## Aerococcus_233 1.804362e-01
## Massilia_214 2.316817e-01
## Nocardioides_95 8.906683e-01
## Sphingomonas_184 2.816820e-01
## Methylophilus_263 1.717961e-01
## Paucibacter_151 4.050033e-01
## Bdellovibrio_194 2.193227e-01
## Afipia_72 8.490495e-01
## Streptococcus_303 2.193227e-01
## Caulobacteraceae_182 1.804362e-01
## Methylobacterium_Methylorubrum_246 2.293155e-01
## Prevotella_histicola_247 2.265902e-01
## Burkholderia_Caballeronia_Paraburkholderia_201 2.382896e-01
## Acetitomaculum_362 1.566496e-01
## Candidatus_Paracaedibacter_347 1.619481e-01
## Hansschlegelia_242 2.316817e-01
## Rhodopseudomonas_156 3.267832e-01
## Aquabacterium_228 2.375204e-01
## Sphingosinicella_163 3.106998e-01
## Methylobacterium_Methylorubrum_61 8.194995e-01
## Sphingobium_205 2.265902e-01
## Corynebacterium_matruchotii_85 3.134710e-01
## Bosea_211 1.578693e-01
## Sphingomonas_367 2.240412e-01
## Blastomonas_131 2.231098e-01
## Thermomonas_hydrothermalis_363 2.193227e-01
## Mesorhizobium_134 4.052295e-01
## Prevotella_248 2.399377e-01
## Pseudoxanthomonas_mexicana_289 2.316817e-01
## Burkholderia_Caballeronia_Paraburkholderia_374 2.240412e-01
## Ensifer_365 2.265902e-01
## Quadrisphaera_271 2.419237e-01
## Kocuria_299 2.316817e-01
## Bosea_231 1.619481e-01
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_199 4.138422e-01
## Caulobacter_272 2.629235e-01
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_314 2.316817e-01
## Abiotrophia_defectiva_181 2.751287e-01
## Granulicatella_167 2.588398e-01
## Klebsiella_245 2.279134e-01
## Pyrinomonas_207 1.717961e-01
## Sphingomonas_141 4.279005e-01
## Fimbriiglobus_223 2.789830e-01
## KD4_96_297 2.458768e-01
## Gemella_213 2.751287e-01
## Brevundimonas_124 2.316817e-01
## Brevundimonas_135 5.008127e-01
## Novosphingobium_311 3.938963e-01
## Roseomonas_cervicalis_243 2.316817e-01
## Kocuria_330 2.458768e-01
## Novispirillum_220 2.316817e-01
## Veillonella_348 2.533865e-01
## Vulcaniibacterium_thermophilum_321 2.629235e-01
## Phenylobacterium_286 2.611728e-01
## Methylobacterium_Methylorubrum_265 2.279134e-01
## Dietzia_109 5.093404e-01
## Burkholderia_Caballeronia_Paraburkholderia_55 8.794813e-01
## UCG_005_354 2.611728e-01
## Stenotrophomonas_115 4.050033e-01
## Vulcaniibacterium_139 2.316817e-01
## Porphyromonas_254 2.929158e-01
## Microbacteriaceae_71 4.505593e-01
## Alloprevotella_219 3.938963e-01
## Flavihumibacter_165 3.102609e-01
## Lawsonella_clevelandensis_143 5.744146e-01
## Pseudoxanthomonas_447 2.789830e-01
## Caulobacteraceae_92 6.362152e-01
## Sphingobium_137 9.302617e-01
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_153 6.930141e-01
## Alphaproteobacteria_187 3.267832e-01
## Bacillus_83 7.051163e-01
## Candidatus_Paracaedibacter_150 3.239117e-01
## Pseudomonas_12 7.480126e-01
## Mesorhizobium_294 4.428501e-01
## Acetobacteraceae_230 6.660379e-01
## Moraxella_37 9.468445e-01
## Staphylococcus_51 8.515403e-01
## Streptococcus_216 5.565940e-01
## Sphingomonas_8 9.267575e-01
## Haemophilus_133 8.490495e-01
## Hydrocarboniphaga_389 8.154718e-01
## Stenotrophomonas_282 8.342425e-01
## Ellin6055_144 8.188330e-01
## Finegoldia_171 8.515403e-01
## Staphylococcus_264 8.083337e-01
## Halomonas_178 8.188330e-01
## Corynebacterium_59 8.761432e-01
## Gammaproteobacteria_226 5.856897e-01
## Rhodococcus_221 8.490495e-01
## Limnobacter_thiooxidans_236 8.515403e-01
## Stenotrophomonas_277 8.194995e-01
## Belnapia_rosea_259 8.906683e-01
## Geodermatophilus_234 8.527899e-01
## Cellulomonas_360 8.761432e-01
## Brachybacterium_200 9.257490e-01
## Micrococcaceae_212 9.214259e-01
## Streptococcus_195 9.257490e-01
## Pseudomonas_154 9.901978e-01
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_343 9.224905e-01
## DSSD61_338 9.274620e-01
## Frankiales_280 9.515011e-01
## Dietzia_timorensis_339 9.621151e-01
## Paenarthrobacter_344 9.622922e-01
## Ruminiclostridium_341 9.665092e-01
## Ralstonia_121 9.799682e-01
## Paracoccus_206 9.747683e-01
## Clostridium_sensu_stricto_1_293 9.907413e-01
## Streptococcus_90 9.988089e-017.2.1.1 STGG vs BLank Volcano plot
EnhancedVolcano(VP.df_SB,lab = rownames(VP.df_SB),x = 'logFC',y = 'adjPvalues',
#selectLab = c("Corynebacterium_44", "Lactococcus_31", "Thermus_thermophilus_10"),
pointSize = 3.0, labSize = 5, labhjust = 0.5, pCutoff = .02, xlim = c(-7, 7), gridlines.major = TRUE, cutoffLineWidth = 0.2,cutoffLineCol = 'grey30', FCcutoff = .5)#+## Warning: One or more p-values is 0. Converting to 10^-1 * current lowest non-
## zero p-value...
#ggsave("VolcanoPlot_fitFeatureModel.tiff", units="in", width=8, height=6, dpi=300, compression = 'lzw')Too many ASVs are identifed as differentially abundant as the batch effect cannot be accounted for in fitFeatureModel
7.2.2 STGG vs RNA_protect
ps_SR <- subset_samples(ps_filtered, sample_data(ps_filtered)$Sample_type!="blank")
ps_SR <- prune_taxa(taxa_sums(ps_SR)>0, ps_SR)
metaSeq_SR <- phyloseq_to_metagenomeSeq(ps_SR) # raw sequence count phyloseq
metaSeq_norm_SR <- cumNorm(metaSeq_SR, p=0.5) # internal normalisation
mod3_SR <- model.matrix(~ Sample_type, pData(metaSeq_norm_SR)) # formula
fit_SR <- fitFeatureModel(metaSeq_norm_SR, mod3_SR) # NOT fitZig; gives higher # false-positives## Warning: Partial NA coefficients for 185 probe(s)## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredVP.df_SR <- MRcoefs(fit_SR, number = Inf, alpha = 1, adjustMethod = "BH") # extract all output (not only significant)
VP.df_SR## logFC se
## Tepidimonas_1 6.97070502 0.2795577
## Curvibacter_2 -6.24110034 0.1640993
## Thermus_5 5.31328439 0.2641883
## Schlegelella_4 -4.43167029 0.3359404
## Thermus_thermophilus_10 4.34493613 0.2748836
## Caulobacter_6 -4.01819870 0.3406502
## Comamonadaceae_7 -3.78823574 0.3184097
## Methylobacterium_Methylorubrum_13 3.71762209 0.5684487
## Bosea_9 -3.62385890 0.3466753
## Sphingobium_11 -3.57184203 0.8125304
## Sphingomonas_8 3.49826241 0.4707445
## Azospirillum_15 -3.20567100 0.2693551
## Ottowia_14 -3.18774879 0.3330265
## Brevundimonas_20 -3.02789241 0.2404451
## Meiothermus_silvanus_16 -2.87911611 0.2478594
## Methyloversatilis_23 -2.85708004 0.2488119
## Lactococcus_31 2.76329154 0.4921029
## Bradyrhizobium_24 2.74128930 0.4752452
## Methylobacterium_Methylorubrum_22 2.59214430 0.6106603
## Methylobacterium_Methylorubrum_26 2.52071130 0.6694787
## Noviherbaspirillum_suwonense_27 2.46627048 0.5861041
## Achromobacter_43 -2.33850457 0.3584676
## Brevundimonas_28 -2.19874042 0.3609625
## Xanthobacter_autotrophicus_29 -2.03969757 0.3673875
## Pseudomonas_japonica_45 1.95867059 0.5294984
## Bacteriovorax_stolpii_38 -1.90187928 0.3212444
## Thermus_3 -1.77189327 0.7118463
## Delftia_21 1.62939247 0.6967945
## Aquabacterium_47 -1.60303726 0.3378383
## Microbacteriaceae_71 -1.57212904 1.1103112
## Phenylobacterium_muchangponense_40 -1.45588757 0.2775335
## Sphingomonas_koreensis_42 -1.43314371 0.6516082
## Rhodopseudomonas_41 1.37002692 0.5900650
## Sphingopyxis_48 -1.31070065 0.2830457
## Aquabacterium_52 -1.29643052 0.3529393
## Comamonas_25 -1.24663060 0.3439378
## Pseudomonas_86 -1.22928284 0.3065503
## Thiobacillus_thioparus_49 -1.22670334 0.2938609
## Proteus_89 -1.21328068 0.3120496
## Brevundimonas_kwangchunensis_65 1.20144086 0.5868756
## Vulcaniibacterium_139 -1.17541331 0.3259796
## Methylobacterium_Methylorubrum_61 1.16717390 0.5060966
## Streptococcus_34 1.11443869 0.6910950
## Sphingomonas_koreensis_116 -1.11276566 0.3167895
## Aquabacterium_parvum_46 -1.10431391 0.3968097
## Moraxella_37 -1.07005629 0.7173571
## Brevundimonas_126 -0.99760750 0.2626333
## Anoxybacillus_102 0.92158229 0.4349409
## Brevundimonas_53 -0.92112062 0.3468231
## Pseudarthrobacter_80 0.91996677 0.5016247
## Dolosigranulum_pigrum_30 -0.91116270 0.7191598
## Pseudomonas_105 -0.87110666 0.2619542
## Meiothermus_58 -0.85390360 0.4491343
## Nitriliruptoraceae_18 0.85026708 0.5783190
## Blastomonas_131 -0.84422493 0.2482504
## Caulobacter_104 0.84362009 0.4713177
## Enhydrobacter_70 0.77791162 0.4997622
## Reyranella_massiliensis_110 -0.75783249 0.2319996
## Gammaproteobacteria_226 -0.75610411 0.2353995
## Bosea_36 0.75156181 0.4488534
## Alcaligenes_60 -0.75126540 0.2495878
## Thermus_brockianus_50 0.73234792 0.5839168
## Candidatus_Paracaedibacter_150 -0.69739840 0.2670142
## Neisseria_63 0.69693216 0.5337677
## Asticcacaulis_excentricus_117 -0.68890891 0.2144400
## Limnohabitans_82 0.68567026 0.5024730
## Cloacibacterium_56 -0.67622960 0.9086949
## Bosea_96 0.66706829 0.4811063
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_64 0.66462104 0.5074419
## Methylobacterium_Methylorubrum_32 0.65828020 0.6380167
## Psychroglaciecola_87 0.63965635 0.4331892
## Acinetobacter_79 0.62890401 0.5417117
## Afipia_72 0.62064142 0.4625793
## Stenotrophomonas_129 -0.61699215 0.9719140
## Methylopilaceae_176 0.59479761 0.3894093
## Staphylococcus_17 -0.58377940 0.5882377
## Meiothermus_152 0.58078591 0.4113512
## Skermanella_aerolata_77 0.56916701 0.4402225
## Sphingobium_81 0.56738249 0.4862287
## Bdellovibrio_194 -0.55697889 0.2162473
## Methylobacterium_Methylorubrum_91 0.54260280 0.4689460
## Phenylobacterium_mobile_101 0.53826081 0.4182635
## Duganella_166 0.52528575 0.3846637
## Corynebacterium_59 -0.51998365 0.4102519
## Burkholderia_Caballeronia_Paraburkholderia_55 0.50890240 0.7677447
## Halomonas_94 0.50243733 0.4508965
## Cupriavidus_78 -0.49702649 0.2643444
## Brevundimonas_124 -0.49702649 0.2643444
## Klenkia_140 0.49344233 0.4086333
## Cupriavidus_gilardii_67 -0.49342368 0.2278791
## Ralstonia_66 0.48577553 0.5632072
## Pseudomonas_145 0.47296785 0.3989543
## Blastococcus_57 0.46724604 0.5405812
## Streptococcus_90 0.46093782 0.3780663
## Brevundimonas_74 0.45199336 0.4054479
## Bosea_73 -0.44793699 0.2127087
## Bosea_vestrisii_39 -0.43772991 0.2662409
## Sphingoaurantiacus_polygranulatus_168 0.43717586 0.3734286
## Enterobacterales_198 0.43487539 0.3755294
## Devosia_148 -0.43313863 0.2375248
## Devosia_288 0.42695319 0.3228906
## Haemophilus_114 0.42501203 0.3893724
## Corynebacterium_35 -0.42243076 0.7018189
## Sphingobium_137 0.42115037 0.3533464
## Sphingopyxis_122 -0.41974505 0.2574876
## Rhodopseudomonas_155 0.41385197 0.3468228
## Anoxybacillus_127 0.41049777 0.4005367
## Variovorax_paradoxus_103 0.40504486 0.4416637
## Caulobacter_169 0.40469615 0.3647312
## Neisseria_118 0.40225695 0.3867020
## Lawsonella_clevelandensis_143 -0.40203961 0.2352482
## Haliangium_107 -0.39675212 0.1962045
## Tepidiphilus_succinatimandens_106 -0.38657386 0.1930070
## Alphaproteobacteria_187 -0.38205766 0.2167636
## Porphyromonas_gingivalis_193 0.37641294 0.3608495
## Frankiales_158 0.37617955 0.3281536
## Brachymonas_75 -0.37577483 0.1896487
## Pyrinomonas_207 -0.37577483 0.1896487
## Corynebacterium_62 0.37336476 0.8622774
## Microvirga_189 0.37270458 0.3764855
## Leifsonia_188 0.35890141 0.3596252
## Yonghaparkia_123 0.35680947 0.3720866
## Methylobacterium_Methylorubrum_240 0.35564666 0.3464484
## Haemophilus_54 0.35459905 0.5150324
## Brevibacillus_208 0.35406110 0.3283611
## Novosphingobium_159 0.34213133 0.3586758
## Galbitalea_203 0.33903108 0.3599674
## Tepidimonas_128 -0.33875732 0.1784318
## Nocardioides_95 0.33358902 0.3838843
## Sphingosinicella_163 -0.33191695 0.2027660
## Sphingobium_yanoikuyae_120 -0.32463455 0.1944096
## Staphylococcus_51 0.32437208 0.4613719
## Stenotrophomonas_115 -0.32404687 0.2573084
## Fluviicola_76 -0.31709561 0.1992218
## Brevundimonas_135 0.31673745 0.3288921
## Pedobacter_381 0.30965642 0.2723438
## Streptococcus_216 -0.30831437 0.2683508
## Devosia_neptuniae_146 0.29987490 0.3267839
## Acidovorax_218 0.29987470 0.3457418
## Turicella_otitidis_268 0.29897567 0.3190660
## Methylobacillus_98 -0.29681603 0.1910477
## Corynebacterium_44 0.29591117 0.4156110
## Flavihumibacter_165 -0.29190165 0.1650010
## Novispirillum_220 -0.29190165 0.1650010
## Luteimonas_209 0.28917621 0.3270533
## Rhodopseudomonas_156 0.28894144 0.3407201
## Streptococcus_303 0.28190357 0.3049791
## Cnuella_349 0.27884487 0.2765651
## Patulibacter_minatonensis_217 0.27490719 0.2943589
## PMMR1_132 -0.27305407 0.1598850
## Massilia_214 0.26983411 0.3340728
## Dietzia_109 0.26881493 0.3619364
## Methylobacterium_Methylorubrum_246 0.26706833 0.3191631
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_199 0.26648968 0.3078305
## Prevotella_histicola_247 0.26470197 0.3164458
## Pseudomonas_154 0.26210093 0.3079683
## Janibacter_147 0.26187876 0.3343308
## Ralstonia_121 -0.26012014 0.3008987
## Acetitomaculum_362 0.25692882 0.2647127
## Mesorhizobium_294 -0.25542587 0.2831383
## Acinetobacter_lwoffii_175 0.25336670 0.3859160
## Sphingomonas_367 0.25282679 0.2821779
## Thermomonas_hydrothermalis_363 0.24960491 0.2775825
## Brevundimonas_149 0.24849926 0.2929445
## Candidatus_Paracaedibacter_347 0.24519746 0.2650427
## Methylophilus_263 0.24476389 0.2847002
## Herbiconiux_185 0.24321306 0.3129305
## Pseudomonas_12 -0.24238157 0.2817117
## Hansschlegelia_242 0.23878899 0.3113100
## Pseudomonas_33 0.23623458 0.6635933
## Burkholderia_Caballeronia_Paraburkholderia_374 0.23527949 0.2699970
## Ensifer_365 0.22930296 0.2717346
## Aerococcus_233 0.22886065 0.2913246
## Burkholderia_Caballeronia_Paraburkholderia_201 0.22297233 0.3239246
## Bacillus_83 0.22296991 0.3922840
## Pseudoxanthomonas_mexicana_289 0.22216466 0.2833979
## Aquabacterium_228 0.21991132 0.3131688
## Kocuria_299 0.21597784 0.2809676
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_314 0.21200870 0.2753780
## Quadrisphaera_271 0.21007764 0.2873930
## Prevotella_248 0.20800522 0.2949114
## Staphylococcus_264 0.20150599 0.2529007
## Roseomonas_cervicalis_243 -0.20030395 0.1420839
## Methylopila_oligotropha_112 0.19951463 0.3548809
## KD4_96_297 0.19789862 0.2755484
## Actinomyces_119 -0.19352141 0.2685084
## Veillonella_348 0.19228956 0.2662633
## Caulobacteraceae_92 -0.18971321 0.3160647
## Finegoldia_171 0.18940770 0.2733132
## Kocuria_330 0.18865768 0.2669600
## Granulicatella_167 -0.18763872 0.3305330
## Rhodococcus_221 -0.17891016 0.2262299
## Micrococcus_97 0.17660055 0.4483290
## Acetobacteraceae_230 0.17495878 0.2747634
## Paracoccus_206 -0.17322877 0.2690298
## Phenylobacterium_286 0.17267372 0.2689487
## Fimbriiglobus_223 0.17162735 0.2945532
## Belnapia_rosea_259 0.16980570 0.2490591
## Escherichia/Shigella_93 0.16614776 0.3166793
## Alloprevotella_219 0.16471180 0.2806883
## UCG_005_354 0.16450446 0.2510324
## Cellulomonas_360 0.16443948 0.2310102
## Abiotrophia_defectiva_181 0.16104023 0.2939412
## Corynebacterium_matruchotii_85 0.15764466 0.3383794
## Klebsiella_245 -0.15482199 0.3279593
## Micrococcaceae_212 0.14214380 0.2717018
## Halomonas_178 0.14181623 0.2902810
## Paucibacter_19 -0.13730890 0.7832094
## Streptococcus_195 0.13689492 0.2762044
## Pseudoxanthomonas_447 0.13678732 0.2211280
## Limnobacter_thiooxidans_236 0.13435151 0.2681055
## Massilia_111 0.13342616 0.4254209
## Geodermatophilus_234 0.13219473 0.2563797
## Porphyromonas_254 0.12903873 0.2497705
## Streptococcus_69 0.11775201 0.2830651
## Stenotrophomonas_277 0.11608446 0.2391488
## Hydrocarboniphaga_389 0.10423832 0.1670795
## Dietzia_timorensis_339 0.10150963 0.2162692
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_153 0.10095900 0.2159897
## Clostridium_sensu_stricto_1_293 0.09870699 0.2190888
## Ruminiclostridium_341 0.08846199 0.1999172
## DSSD61_338 0.08261149 0.1914059
## Stenotrophomonas_282 0.07955370 0.2043566
## Haemophilus_133 0.07944131 0.1897275
## Ellin6055_144 0.07787436 0.2239909
## Sphingomonas_141 -0.06987880 0.3990292
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_343 0.06688862 0.1878471
## Mesorhizobium_134 -0.06651635 0.4362674
## Frankiales_280 0.06613523 0.2001688
## Brachybacterium_200 -0.06418508 0.2754103
## Gemella_213 -0.04821954 0.3189291
## Paenarthrobacter_344 0.03570582 0.1821390
## Sphingomonadaceae_84 -0.02194046 0.3233208
## Acinetobacter_88 0.01804049 0.4297335
## pvalues
## Tepidimonas_1 0.000000e+00
## Curvibacter_2 0.000000e+00
## Thermus_5 0.000000e+00
## Schlegelella_4 0.000000e+00
## Thermus_thermophilus_10 0.000000e+00
## Caulobacter_6 0.000000e+00
## Comamonadaceae_7 0.000000e+00
## Methylobacterium_Methylorubrum_13 6.154233e-11
## Bosea_9 0.000000e+00
## Sphingobium_11 1.102899e-05
## Sphingomonas_8 1.074696e-13
## Azospirillum_15 0.000000e+00
## Ottowia_14 0.000000e+00
## Brevundimonas_20 0.000000e+00
## Meiothermus_silvanus_16 0.000000e+00
## Methyloversatilis_23 0.000000e+00
## Lactococcus_31 1.962530e-08
## Bradyrhizobium_24 8.014267e-09
## Methylobacterium_Methylorubrum_22 2.187669e-05
## Methylobacterium_Methylorubrum_26 1.664256e-04
## Noviherbaspirillum_suwonense_27 2.577489e-05
## Achromobacter_43 6.863310e-11
## Brevundimonas_28 1.119791e-09
## Xanthobacter_autotrophicus_29 2.825857e-08
## Pseudomonas_japonica_45 2.163607e-04
## Bacteriovorax_stolpii_38 3.212567e-09
## Thermus_3 1.280485e-02
## Delftia_21 1.936589e-02
## Aquabacterium_47 2.085233e-06
## Microbacteriaceae_71 1.567944e-01
## Phenylobacterium_muchangponense_40 1.555992e-07
## Sphingomonas_koreensis_42 2.784986e-02
## Rhodopseudomonas_41 2.024242e-02
## Sphingopyxis_48 3.644255e-06
## Aquabacterium_52 2.394954e-04
## Comamonas_25 2.894297e-04
## Pseudomonas_86 6.070521e-05
## Thiobacillus_thioparus_49 2.987254e-05
## Proteus_89 1.010311e-04
## Brevundimonas_kwangchunensis_65 4.064029e-02
## Vulcaniibacterium_139 3.112068e-04
## Methylobacterium_Methylorubrum_61 2.109792e-02
## Streptococcus_34 1.068381e-01
## Sphingomonas_koreensis_116 4.436886e-04
## Aquabacterium_parvum_46 5.386191e-03
## Moraxella_37 1.357870e-01
## Brevundimonas_126 1.455858e-04
## Anoxybacillus_102 3.410164e-02
## Brevundimonas_53 7.910164e-03
## Pseudarthrobacter_80 6.665784e-02
## Dolosigranulum_pigrum_30 2.051616e-01
## Pseudomonas_105 8.828687e-04
## Meiothermus_58 5.727307e-02
## Nitriliruptoraceae_18 1.414971e-01
## Blastomonas_131 6.721382e-04
## Caulobacter_104 7.346710e-02
## Enhydrobacter_70 1.195742e-01
## Reyranella_massiliensis_110 1.088764e-03
## Gammaproteobacteria_226 1.318127e-03
## Bosea_36 9.405131e-02
## Alcaligenes_60 2.612262e-03
## Thermus_brockianus_50 2.097696e-01
## Candidatus_Paracaedibacter_150 9.005625e-03
## Neisseria_63 1.916599e-01
## Asticcacaulis_excentricus_117 1.315417e-03
## Limnohabitans_82 1.723816e-01
## Cloacibacterium_56 4.567695e-01
## Bosea_96 1.655852e-01
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_64 1.902811e-01
## Methylobacterium_Methylorubrum_32 3.021845e-01
## Psychroglaciecola_87 1.397772e-01
## Acinetobacter_79 2.456593e-01
## Afipia_72 1.796941e-01
## Stenotrophomonas_129 5.255447e-01
## Methylopilaceae_176 1.266527e-01
## Staphylococcus_17 3.209923e-01
## Meiothermus_152 1.579800e-01
## Skermanella_aerolata_77 1.960430e-01
## Sphingobium_81 2.432489e-01
## Bdellovibrio_194 1.000498e-02
## Methylobacterium_Methylorubrum_91 2.472443e-01
## Phenylobacterium_mobile_101 1.981313e-01
## Duganella_166 1.720735e-01
## Corynebacterium_59 2.049859e-01
## Burkholderia_Caballeronia_Paraburkholderia_55 5.074243e-01
## Halomonas_94 2.651473e-01
## Cupriavidus_78 6.007764e-02
## Brevundimonas_124 6.007764e-02
## Klenkia_140 2.272231e-01
## Cupriavidus_gilardii_67 3.036567e-02
## Ralstonia_66 3.884033e-01
## Pseudomonas_145 2.358123e-01
## Blastococcus_57 3.874010e-01
## Streptococcus_90 2.227689e-01
## Brevundimonas_74 2.649361e-01
## Bosea_73 3.521562e-02
## Bosea_vestrisii_39 1.001531e-01
## Sphingoaurantiacus_polygranulatus_168 2.417161e-01
## Enterobacterales_198 2.468506e-01
## Devosia_148 6.822000e-02
## Devosia_288 1.860735e-01
## Haemophilus_114 2.750393e-01
## Corynebacterium_35 5.472350e-01
## Sphingobium_137 2.333039e-01
## Sphingopyxis_122 1.030684e-01
## Rhodopseudomonas_155 2.327650e-01
## Anoxybacillus_127 3.054249e-01
## Variovorax_paradoxus_103 3.590960e-01
## Caulobacter_169 2.671828e-01
## Neisseria_118 2.982355e-01
## Lawsonella_clevelandensis_143 8.745059e-02
## Haliangium_107 4.316237e-02
## Tepidiphilus_succinatimandens_106 4.518792e-02
## Alphaproteobacteria_187 7.797568e-02
## Porphyromonas_gingivalis_193 2.968881e-01
## Frankiales_158 2.516495e-01
## Brachymonas_75 4.754354e-02
## Pyrinomonas_207 4.754354e-02
## Corynebacterium_62 6.650159e-01
## Microvirga_189 3.221949e-01
## Leifsonia_188 3.182855e-01
## Yonghaparkia_123 3.375879e-01
## Methylobacterium_Methylorubrum_240 3.046323e-01
## Haemophilus_54 4.911389e-01
## Brevibacillus_208 2.809144e-01
## Novosphingobium_159 3.401476e-01
## Galbitalea_203 3.462755e-01
## Tepidimonas_128 5.762688e-02
## Nocardioides_95 3.848563e-01
## Sphingosinicella_163 1.016417e-01
## Sphingobium_yanoikuyae_120 9.494936e-02
## Staphylococcus_51 4.820184e-01
## Stenotrophomonas_115 2.078962e-01
## Fluviicola_76 1.114586e-01
## Brevundimonas_135 3.355256e-01
## Pedobacter_381 2.555359e-01
## Streptococcus_216 2.505878e-01
## Devosia_neptuniae_146 3.587995e-01
## Acidovorax_218 3.857573e-01
## Turicella_otitidis_268 3.487412e-01
## Methylobacillus_98 1.202746e-01
## Corynebacterium_44 4.764706e-01
## Flavihumibacter_165 7.687880e-02
## Novispirillum_220 7.687880e-02
## Luteimonas_209 3.765954e-01
## Rhodopseudomonas_156 3.964203e-01
## Streptococcus_303 3.553108e-01
## Cnuella_349 3.133377e-01
## Patulibacter_minatonensis_217 3.503460e-01
## PMMR1_132 8.767066e-02
## Massilia_214 4.192572e-01
## Dietzia_109 4.576553e-01
## Methylobacterium_Methylorubrum_246 4.027179e-01
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_199 3.866533e-01
## Prevotella_histicola_247 4.028824e-01
## Pseudomonas_154 3.947334e-01
## Janibacter_147 4.334554e-01
## Ralstonia_121 3.873256e-01
## Acetitomaculum_362 3.317500e-01
## Mesorhizobium_294 3.669910e-01
## Acinetobacter_lwoffii_175 5.114810e-01
## Sphingomonas_367 3.702614e-01
## Thermomonas_hydrothermalis_363 3.685408e-01
## Brevundimonas_149 3.962815e-01
## Candidatus_Paracaedibacter_347 3.549012e-01
## Methylophilus_263 3.899407e-01
## Herbiconiux_185 4.370342e-01
## Pseudomonas_12 3.895747e-01
## Hansschlegelia_242 4.430544e-01
## Pseudomonas_33 7.218458e-01
## Burkholderia_Caballeronia_Paraburkholderia_374 3.835275e-01
## Ensifer_365 3.987539e-01
## Aerococcus_233 4.321098e-01
## Burkholderia_Caballeronia_Paraburkholderia_201 4.912347e-01
## Bacillus_83 5.697709e-01
## Pseudoxanthomonas_mexicana_289 4.330801e-01
## Aquabacterium_228 4.825461e-01
## Kocuria_299 4.420755e-01
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_314 4.413696e-01
## Quadrisphaera_271 4.647933e-01
## Prevotella_248 4.806147e-01
## Staphylococcus_264 4.255793e-01
## Roseomonas_cervicalis_243 1.586110e-01
## Methylopila_oligotropha_112 5.739786e-01
## KD4_96_297 4.726346e-01
## Actinomyces_119 4.710772e-01
## Veillonella_348 4.701849e-01
## Caulobacteraceae_92 5.483494e-01
## Finegoldia_171 4.883057e-01
## Kocuria_330 4.797598e-01
## Granulicatella_167 5.702487e-01
## Rhodococcus_221 4.290413e-01
## Micrococcus_97 6.936487e-01
## Acetobacteraceae_230 5.242802e-01
## Paracoccus_206 5.196391e-01
## Phenylobacterium_286 5.208523e-01
## Fimbriiglobus_223 5.601154e-01
## Belnapia_rosea_259 4.953726e-01
## Escherichia/Shigella_93 5.998222e-01
## Alloprevotella_219 5.573288e-01
## UCG_005_354 5.122670e-01
## Cellulomonas_360 4.765713e-01
## Abiotrophia_defectiva_181 5.837844e-01
## Corynebacterium_matruchotii_85 6.413004e-01
## Klebsiella_245 6.368719e-01
## Micrococcaceae_212 6.008621e-01
## Halomonas_178 6.251617e-01
## Paucibacter_19 8.608316e-01
## Streptococcus_195 6.201562e-01
## Pseudoxanthomonas_447 5.361872e-01
## Limnobacter_thiooxidans_236 6.162906e-01
## Massilia_111 7.537996e-01
## Geodermatophilus_234 6.061193e-01
## Porphyromonas_254 6.054150e-01
## Streptococcus_69 6.774180e-01
## Stenotrophomonas_277 6.273878e-01
## Hydrocarboniphaga_389 5.327033e-01
## Dietzia_timorensis_339 6.388073e-01
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_153 6.401958e-01
## Clostridium_sensu_stricto_1_293 6.523254e-01
## Ruminiclostridium_341 6.581324e-01
## DSSD61_338 6.660294e-01
## Stenotrophomonas_282 6.970626e-01
## Haemophilus_133 6.754261e-01
## Ellin6055_144 7.280898e-01
## Sphingomonas_141 8.609837e-01
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_343 7.217805e-01
## Mesorhizobium_134 8.788187e-01
## Frankiales_280 7.410998e-01
## Brachybacterium_200 8.157206e-01
## Gemella_213 8.798242e-01
## Paenarthrobacter_344 8.445820e-01
## Sphingomonadaceae_84 9.458973e-01
## Acinetobacter_88 9.665141e-01
## adjPvalues
## Tepidimonas_1 0.000000e+00
## Curvibacter_2 0.000000e+00
## Thermus_5 0.000000e+00
## Schlegelella_4 0.000000e+00
## Thermus_thermophilus_10 0.000000e+00
## Caulobacter_6 0.000000e+00
## Comamonadaceae_7 0.000000e+00
## Methylobacterium_Methylorubrum_13 9.600603e-10
## Bosea_9 0.000000e+00
## Sphingobium_11 1.032314e-04
## Sphingomonas_8 1.796277e-12
## Azospirillum_15 0.000000e+00
## Ottowia_14 0.000000e+00
## Brevundimonas_20 0.000000e+00
## Meiothermus_silvanus_16 0.000000e+00
## Methyloversatilis_23 0.000000e+00
## Lactococcus_31 2.296160e-07
## Bradyrhizobium_24 9.870203e-08
## Methylobacterium_Methylorubrum_22 1.968903e-04
## Methylobacterium_Methylorubrum_26 1.216987e-03
## Noviherbaspirillum_suwonense_27 2.233824e-04
## Achromobacter_43 1.003759e-09
## Brevundimonas_28 1.541360e-08
## Xanthobacter_autotrophicus_29 3.148812e-07
## Pseudomonas_japonica_45 1.534194e-03
## Bacteriovorax_stolpii_38 4.176337e-08
## Thermus_3 6.242362e-02
## Delftia_21 9.248201e-02
## Aquabacterium_47 2.121498e-05
## Microbacteriaceae_71 4.217611e-01
## Phenylobacterium_muchangponense_40 1.655010e-06
## Sphingomonas_koreensis_42 1.253244e-01
## Rhodopseudomonas_41 9.473454e-02
## Sphingopyxis_48 3.553149e-05
## Aquabacterium_52 1.648292e-03
## Comamonas_25 1.935044e-03
## Pseudomonas_86 4.898282e-04
## Thiobacillus_thioparus_49 2.496491e-04
## Proteus_89 7.880428e-04
## Brevundimonas_kwangchunensis_65 1.698183e-01
## Vulcaniibacterium_139 2.022844e-03
## Methylobacterium_Methylorubrum_61 9.680221e-02
## Streptococcus_34 3.205142e-01
## Sphingomonas_koreensis_116 2.806031e-03
## Aquabacterium_parvum_46 2.864475e-02
## Moraxella_37 3.828213e-01
## Brevundimonas_126 1.098938e-03
## Anoxybacillus_102 1.477738e-01
## Brevundimonas_53 4.113286e-02
## Pseudarthrobacter_80 2.399682e-01
## Dolosigranulum_pigrum_30 4.849275e-01
## Pseudomonas_105 5.297212e-03
## Meiothermus_58 2.174950e-01
## Nitriliruptoraceae_18 3.895332e-01
## Blastomonas_131 4.138957e-03
## Caulobacter_104 2.565866e-01
## Enhydrobacter_70 3.474598e-01
## Reyranella_massiliensis_110 6.369270e-03
## Gammaproteobacteria_226 7.343853e-03
## Bosea_36 3.002453e-01
## Alcaligenes_60 1.421556e-02
## Thermus_brockianus_50 4.860009e-01
## Candidatus_Paracaedibacter_150 4.581122e-02
## Neisseria_63 4.720887e-01
## Asticcacaulis_excentricus_117 7.343853e-03
## Limnohabitans_82 4.432670e-01
## Cloacibacterium_56 6.273100e-01
## Bosea_96 4.353587e-01
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_64 4.720887e-01
## Methylobacterium_Methylorubrum_32 5.763663e-01
## Psychroglaciecola_87 3.893793e-01
## Acinetobacter_79 5.211148e-01
## Afipia_72 4.570482e-01
## Stenotrophomonas_129 6.438610e-01
## Methylopilaceae_176 3.614237e-01
## Staphylococcus_17 5.890126e-01
## Meiothermus_152 4.217611e-01
## Skermanella_aerolata_77 4.778548e-01
## Sphingobium_81 5.211148e-01
## Bdellovibrio_194 4.981204e-02
## Methylobacterium_Methylorubrum_91 5.211148e-01
## Phenylobacterium_mobile_101 4.779662e-01
## Duganella_166 4.432670e-01
## Corynebacterium_59 4.849275e-01
## Burkholderia_Caballeronia_Paraburkholderia_55 6.410186e-01
## Halomonas_94 5.343656e-01
## Cupriavidus_78 2.196589e-01
## Brevundimonas_124 2.196589e-01
## Klenkia_140 5.162156e-01
## Cupriavidus_gilardii_67 1.340673e-01
## Ralstonia_66 5.966740e-01
## Pseudomonas_145 5.205668e-01
## Blastococcus_57 5.966740e-01
## Streptococcus_90 5.110581e-01
## Brevundimonas_74 5.343656e-01
## Bosea_73 1.498265e-01
## Bosea_vestrisii_39 3.124775e-01
## Sphingoaurantiacus_polygranulatus_168 5.211148e-01
## Enterobacterales_198 5.211148e-01
## Devosia_148 2.418709e-01
## Devosia_288 4.681849e-01
## Haemophilus_114 5.454170e-01
## Corynebacterium_35 6.580193e-01
## Sphingobium_137 5.199345e-01
## Sphingopyxis_122 3.132210e-01
## Rhodopseudomonas_155 5.199345e-01
## Anoxybacillus_127 5.763663e-01
## Variovorax_paradoxus_103 5.966740e-01
## Caulobacter_169 5.343656e-01
## Neisseria_118 5.763663e-01
## Lawsonella_clevelandensis_143 2.849297e-01
## Haliangium_107 1.771929e-01
## Tepidiphilus_succinatimandens_106 1.823099e-01
## Alphaproteobacteria_187 2.606615e-01
## Porphyromonas_gingivalis_193 5.763663e-01
## Frankiales_158 5.211148e-01
## Brachymonas_75 1.854198e-01
## Pyrinomonas_207 1.854198e-01
## Corynebacterium_62 7.182068e-01
## Microvirga_189 5.890126e-01
## Leifsonia_188 5.890126e-01
## Yonghaparkia_123 5.966740e-01
## Methylobacterium_Methylorubrum_240 5.763663e-01
## Haemophilus_54 6.281361e-01
## Brevibacillus_208 5.523862e-01
## Novosphingobium_159 5.966740e-01
## Galbitalea_203 5.966740e-01
## Tepidimonas_128 2.174950e-01
## Nocardioides_95 5.966740e-01
## Sphingosinicella_163 3.129496e-01
## Sphingobium_yanoikuyae_120 3.002453e-01
## Staphylococcus_51 6.273100e-01
## Stenotrophomonas_115 4.860009e-01
## Fluviicola_76 3.301432e-01
## Brevundimonas_135 5.966740e-01
## Pedobacter_381 5.245210e-01
## Streptococcus_216 5.211148e-01
## Devosia_neptuniae_146 5.966740e-01
## Acidovorax_218 5.966740e-01
## Turicella_otitidis_268 5.966740e-01
## Methylobacillus_98 3.474598e-01
## Corynebacterium_44 6.273100e-01
## Flavihumibacter_165 2.606615e-01
## Novispirillum_220 2.606615e-01
## Luteimonas_209 5.966740e-01
## Rhodopseudomonas_156 5.966740e-01
## Streptococcus_303 5.966740e-01
## Cnuella_349 5.865682e-01
## Patulibacter_minatonensis_217 5.966740e-01
## PMMR1_132 2.849297e-01
## Massilia_214 6.170201e-01
## Dietzia_109 6.273100e-01
## Methylobacterium_Methylorubrum_246 5.966740e-01
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_199 5.966740e-01
## Prevotella_histicola_247 5.966740e-01
## Pseudomonas_154 5.966740e-01
## Janibacter_147 6.171115e-01
## Ralstonia_121 5.966740e-01
## Acetitomaculum_362 5.966740e-01
## Mesorhizobium_294 5.966740e-01
## Acinetobacter_lwoffii_175 6.410186e-01
## Sphingomonas_367 5.966740e-01
## Thermomonas_hydrothermalis_363 5.966740e-01
## Brevundimonas_149 5.966740e-01
## Candidatus_Paracaedibacter_347 5.966740e-01
## Methylophilus_263 5.966740e-01
## Herbiconiux_185 6.171115e-01
## Pseudomonas_12 5.966740e-01
## Hansschlegelia_242 6.171115e-01
## Pseudomonas_33 7.574525e-01
## Burkholderia_Caballeronia_Paraburkholderia_374 5.966740e-01
## Ensifer_365 5.966740e-01
## Aerococcus_233 6.171115e-01
## Burkholderia_Caballeronia_Paraburkholderia_201 6.281361e-01
## Bacillus_83 6.705437e-01
## Pseudoxanthomonas_mexicana_289 6.171115e-01
## Aquabacterium_228 6.273100e-01
## Kocuria_299 6.171115e-01
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_314 6.171115e-01
## Quadrisphaera_271 6.273100e-01
## Prevotella_248 6.273100e-01
## Staphylococcus_264 6.171115e-01
## Roseomonas_cervicalis_243 4.217611e-01
## Methylopila_oligotropha_112 6.715550e-01
## KD4_96_297 6.273100e-01
## Actinomyces_119 6.273100e-01
## Veillonella_348 6.273100e-01
## Caulobacteraceae_92 6.580193e-01
## Finegoldia_171 6.281361e-01
## Kocuria_330 6.273100e-01
## Granulicatella_167 6.705437e-01
## Rhodococcus_221 6.171115e-01
## Micrococcus_97 7.377900e-01
## Acetobacteraceae_230 6.438610e-01
## Paracoccus_206 6.438610e-01
## Phenylobacterium_286 6.438610e-01
## Fimbriiglobus_223 6.653147e-01
## Belnapia_rosea_259 6.299847e-01
## Escherichia/Shigella_93 6.918630e-01
## Alloprevotella_219 6.653147e-01
## UCG_005_354 6.410186e-01
## Cellulomonas_360 6.273100e-01
## Abiotrophia_defectiva_181 6.796295e-01
## Corynebacterium_matruchotii_85 7.045272e-01
## Klebsiella_245 7.045272e-01
## Micrococcaceae_212 6.918630e-01
## Halomonas_178 7.024342e-01
## Paucibacter_19 8.759574e-01
## Streptococcus_195 7.010461e-01
## Pseudoxanthomonas_447 6.500923e-01
## Limnobacter_thiooxidans_236 7.000583e-01
## Massilia_111 7.804828e-01
## Geodermatophilus_234 6.918630e-01
## Porphyromonas_254 6.918630e-01
## Streptococcus_69 7.238165e-01
## Stenotrophomonas_277 7.024342e-01
## Hydrocarboniphaga_389 6.492321e-01
## Dietzia_timorensis_339 7.045272e-01
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_153 7.045272e-01
## Clostridium_sensu_stricto_1_293 7.132903e-01
## Ruminiclostridium_341 7.162929e-01
## DSSD61_338 7.182068e-01
## Stenotrophomonas_282 7.380663e-01
## Haemophilus_133 7.238165e-01
## Ellin6055_144 7.605938e-01
## Sphingomonas_141 8.759574e-01
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_343 7.574525e-01
## Mesorhizobium_134 8.874089e-01
## Frankiales_280 7.707438e-01
## Brachybacterium_200 8.408749e-01
## Gemella_213 8.874089e-01
## Paenarthrobacter_344 8.668078e-01
## Sphingomonadaceae_84 9.499569e-01
## Acinetobacter_88 9.665141e-017.2.3 RNA_protect vs Blanks
ps_RB <- subset_samples(ps_filtered, sample_data(ps_filtered)$Sample_type!="stgg")
ps_RB <- prune_taxa(taxa_sums(ps_RB)>0, ps_RB)
metaSeq_RB <- phyloseq_to_metagenomeSeq(ps_RB) # raw sequence count phyloseq
metaSeq_norm_RB <- cumNorm(metaSeq_RB, p=0.5) # internal normalisation
mod3_RB <- model.matrix(~ Sample_type, pData(metaSeq_norm_RB)) # formula
fit_RB <- fitFeatureModel(metaSeq_norm_RB, mod3_RB) # NOT fitZig; gives higher # false-positives## Warning: Partial NA coefficients for 85 probe(s)## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredVP.df_RB <- MRcoefs(fit_RB, number = Inf, alpha = 1, adjustMethod = "BH") # extract all output (not only significant)
VP.df_RB## logFC se
## Vulcaniibacterium_139 2.570735242 0.95683953
## Corynebacterium_35 2.533100274 0.99471296
## Cloacibacterium_56 2.160506204 0.89104038
## Tepidimonas_1 -2.047247042 0.70935560
## Pseudomonas_86 1.476049129 0.74792980
## Sphingomonas_8 -1.475388140 0.84096913
## Sphingomonas_koreensis_116 1.440616875 0.83213340
## Blastomonas_131 1.358954365 0.97413591
## Thermus_5 -1.292086784 0.65654247
## Bosea_36 -1.253948071 0.55875457
## Proteus_89 1.195858087 0.73415837
## Aquabacterium_47 1.132469538 0.64119195
## Methylobacterium_Methylorubrum_13 -1.099869094 0.58139976
## Pseudomonas_33 1.099175100 0.74011255
## Achromobacter_43 1.043224544 0.42872007
## Corynebacterium_44 -1.022236329 0.56966433
## Moraxella_37 0.983610734 0.58156649
## Meiothermus_58 0.966409749 0.84902146
## Pseudomonas_105 0.965392715 0.86106042
## Brevundimonas_126 0.849255490 0.68301594
## Reyranella_massiliensis_110 0.812554451 0.76088015
## Noviherbaspirillum_suwonense_27 -0.809465524 0.54024159
## Microbacteriaceae_71 0.737236364 0.28002704
## Dolosigranulum_pigrum_30 0.662970717 0.47871877
## Bdellovibrio_194 0.651310675 0.95736194
## Bradyrhizobium_24 -0.607967852 0.59361609
## Stenotrophomonas_129 0.605025788 0.20241669
## Brevundimonas_28 0.604451186 0.49716432
## Corynebacterium_62 0.591287197 0.96142829
## Delftia_21 -0.589139340 0.70494447
## Asticcacaulis_excentricus_117 0.563265221 0.75652974
## Methylobacterium_Methylorubrum_22 -0.562203141 0.47302851
## Bosea_vestrisii_39 -0.553529644 0.44139091
## Streptococcus_69 -0.545232316 0.43514067
## Methylobacterium_Methylorubrum_61 -0.538511974 0.45122908
## Meiothermus_silvanus_16 -0.530428847 0.37491405
## Aquabacterium_52 0.508952974 0.55216906
## Gammaproteobacteria_226 0.507931676 0.19540666
## Nitriliruptoraceae_18 -0.499478159 0.40425203
## Mesorhizobium_134 0.495340748 0.32796244
## Brevundimonas_53 0.488851820 0.86180619
## Candidatus_Paracaedibacter_150 0.457160134 0.24791782
## Brevundimonas_74 -0.425118341 0.38535126
## Phenylobacterium_muchangponense_40 -0.409096622 0.47087752
## Afipia_72 -0.403953587 0.38097790
## Sphingopyxis_48 0.398849662 0.63061103
## Thermus_3 0.389735906 0.43169474
## Corynebacterium_59 0.382175783 0.33311679
## Rhodopseudomonas_41 -0.370486175 0.42267281
## Methylobacterium_Methylorubrum_26 -0.345837857 0.40028498
## Sphingomonas_koreensis_42 0.344924844 0.49035599
## Fluviicola_76 -0.324801580 0.34446796
## Staphylococcus_51 -0.315812318 0.29042345
## Cupriavidus_gilardii_67 -0.313470358 0.38718041
## Klebsiella_245 0.309689380 0.16619129
## Bosea_73 -0.305870175 0.37967554
## Bacteriovorax_stolpii_38 0.302912790 0.49068563
## Thermus_thermophilus_10 -0.302145242 0.33955132
## Pseudomonas_japonica_45 -0.294796514 0.34525318
## Acinetobacter_100 -0.293395564 0.26243339
## Mesorhizobium_294 0.289473876 0.15771731
## Granulicatella_167 0.283287296 0.15562541
## Streptococcus_216 0.271791669 0.15067036
## Sphingomonas_141 0.270617673 0.25293230
## Curvibacter_2 0.263927038 0.28729146
## Enhydrobacter_70 -0.259141996 0.27262810
## Paucibacter_151 -0.258117705 0.36986267
## Burkholderia_Caballeronia_Paraburkholderia_55 -0.257078430 0.90715734
## Acinetobacter_88 0.255230757 0.16763518
## Methylobacillus_98 -0.254805742 0.29933217
## Phreatobacter_oligotrophus_186 -0.246527244 0.22425458
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_153 -0.244821654 0.35371944
## Brevundimonas_124 0.243965034 0.26817978
## Providencia_130 -0.239230282 0.20447824
## Tepidiphilus_232 -0.234211207 0.23372858
## Streptococcus_90 -0.228754002 0.33494323
## Sphingomonas_184 -0.226227282 0.29347041
## Escherichia/Shigella_93 -0.219930468 0.29085409
## Methylobacterium_Methylorubrum_32 -0.217870065 0.46389921
## Brevundimonas_149 -0.214028122 0.31242640
## Legionella_lytica_244 -0.209614101 0.20372389
## Gemella_213 0.208395177 0.12019528
## Sphingobium_137 -0.204559936 0.29747780
## Nocardioides_95 -0.204223758 0.32096142
## Brachymonas_75 -0.203802910 0.36044350
## Ottowia_14 0.203379661 0.36529680
## PMMR1_132 -0.202409584 0.32236495
## Pseudomonas_12 0.202216003 0.35089621
## Caulobacteraceae_182 -0.193845002 0.21431279
## Psychroglaciecola_87 -0.192724220 0.27679652
## Phenylobacterium_mobile_101 -0.192476372 0.26413608
## Novosphingobium_311 -0.190127073 0.27181239
## Alphaproteobacteria_187 0.189697255 0.21886759
## Haliangium_107 -0.188457519 0.27427449
## Pseudomonas_154 -0.185979026 0.25829496
## Sphingobium_205 -0.185482084 0.21998457
## Haemophilus_114 -0.184304328 0.25099241
## Lawsonella_clevelandensis_143 0.182002073 0.34600320
## Sphingomonadaceae_84 -0.179476675 0.31554721
## Caulobacter_272 -0.175912211 0.22510384
## Hydrocarboniphaga_389 -0.174337448 0.27160628
## Neisseria_118 -0.168519849 0.22516149
## Bosea_211 -0.168169011 0.17812175
## Comamonas_25 -0.165043243 0.58591175
## Stenotrophomonas_282 -0.164361962 0.25061295
## Caulobacter_104 -0.163776256 0.26031128
## Xanthobacter_autotrophicus_29 0.162328103 0.38927360
## Haemophilus_133 -0.161205895 0.34004014
## Brevundimonas_20 -0.160987929 0.37312335
## Vulcaniibacterium_thermophilum_321 -0.159446069 0.20402620
## Bosea_231 -0.155879197 0.16678773
## Azospirillum_15 -0.154160437 94.57880746
## Paracoccus_206 0.153529506 0.19230092
## Methylobacterium_Methylorubrum_265 -0.148195184 0.17742308
## Caulobacter_6 0.139905896 0.35308656
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_64 -0.135189111 0.38432997
## Frankiales_158 -0.132841731 0.15620037
## Methyloversatilis_23 0.132794320 0.35718296
## Ralstonia_121 0.128741595 0.27956849
## Finegoldia_171 -0.127686077 0.26527801
## Blastococcus_57 0.126636576 0.27211780
## Ellin6055_144 -0.126199237 0.20800389
## Halomonas_94 -0.125939782 0.28515468
## Massilia_111 0.121327354 0.28521509
## Brevundimonas_135 -0.116224895 0.24205879
## Belnapia_rosea_259 -0.113646447 0.25727733
## Limnohabitans_82 -0.111556675 0.22601436
## Aquabacterium_parvum_46 -0.110292978 0.63796998
## Rhodopseudomonas_155 -0.108540165 0.23499590
## Tepidimonas_128 -0.108123542 0.27044754
## Acinetobacter_lwoffii_175 0.106422112 0.17296915
## Anoxybacillus_127 -0.103541340 0.20513367
## Streptococcus_195 -0.103314974 0.20428978
## Bacillus_83 -0.101985423 0.23303214
## Dietzia_109 -0.100481531 0.17632700
## Sphingopyxis_122 0.097905370 0.30242817
## Novosphingobium_159 -0.096412303 0.23176276
## Staphylococcus_17 -0.095335144 0.41451792
## Acinetobacter_79 0.095063419 0.21462222
## DSSD61_338 -0.094113427 0.18883199
## Micrococcaceae_212 -0.093774730 0.17371620
## Ruminiclostridium_341 -0.093570141 0.19354444
## Pyrinomonas_207 0.091067442 0.20187033
## Bosea_9 0.090635593 0.33623530
## Clostridium_sensu_stricto_1_293 -0.090386685 0.18244950
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_343 -0.090386685 0.18244950
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_199 -0.089356058 0.20707362
## Duganella_166 -0.086512957 0.21035572
## Cellulomonas_360 -0.086266415 0.17544035
## Devosia_148 0.084820589 0.31825309
## Comamonadaceae_7 -0.084542940 0.36607868
## Paenarthrobacter_344 -0.082326352 0.18918911
## Acetobacteraceae_230 -0.081659589 0.16766976
## Limnobacter_thiooxidans_236 -0.080659830 0.16467114
## Dietzia_timorensis_339 -0.080659830 0.16467114
## Micrococcus_97 0.079918633 0.30160159
## Thiobacillus_thioparus_49 0.079460831 0.57088962
## Stenotrophomonas_115 0.078796843 0.24521755
## Staphylococcus_264 -0.074181535 0.12865680
## Rhodococcus_221 0.074091858 0.22725629
## Flavihumibacter_165 0.073880782 0.19911192
## Rhodopseudomonas_156 -0.073840920 0.15894649
## Frankiales_280 -0.073468631 0.13437144
## Halomonas_178 -0.067957831 0.12131175
## Geodermatophilus_234 -0.067957831 0.12131175
## Sphingobium_yanoikuyae_120 -0.064211960 0.29117476
## Skermanella_aerolata_77 -0.063266326 0.13757244
## Tepidiphilus_succinatimandens_106 -0.063018190 0.29706660
## Ralstonia_66 0.059779392 0.39084078
## Schlegelella_4 -0.059022596 0.31664331
## Sphingosinicella_163 0.055361490 0.30649679
## Stenotrophomonas_277 -0.052432948 0.10410270
## Alcaligenes_60 -0.048596557 0.71993516
## Novispirillum_220 0.039197227 0.20603260
## Brachybacterium_200 0.036454602 0.30901775
## Roseomonas_cervicalis_243 -0.031388889 0.19795852
## Actinomyces_119 -0.031347062 0.27786910
## Alloprevotella_219 -0.029445895 0.08325272
## Cupriavidus_78 0.029089505 0.38797471
## Streptococcus_34 0.027839756 0.37939224
## Paucibacter_19 -0.020290988 0.33424461
## Sphingobium_11 0.008623674 94.57869486
## Caulobacteraceae_92 0.004610335 0.29915165
## pvalues adjPvalues
## Vulcaniibacterium_139 0.007216298 0.3318072
## Corynebacterium_35 0.010878925 0.3318072
## Cloacibacterium_56 0.015321013 0.3504682
## Tepidimonas_1 0.003900904 0.3318072
## Pseudomonas_86 0.048437153 0.6938212
## Sphingomonas_8 0.079363642 0.6938212
## Sphingomonas_koreensis_116 0.083410200 0.6938212
## Blastomonas_131 0.163005075 0.8362421
## Thermus_5 0.049066075 0.6938212
## Bosea_36 0.024820564 0.5046848
## Proteus_89 0.103337790 0.7879506
## Aquabacterium_47 0.077363266 0.6938212
## Methylobacterium_Methylorubrum_13 0.058522900 0.6938212
## Pseudomonas_33 0.137505184 0.8362421
## Achromobacter_43 0.014959971 0.3504682
## Corynebacterium_44 0.072740765 0.6938212
## Moraxella_37 0.090777114 0.7222701
## Meiothermus_58 0.255010664 0.8362421
## Pseudomonas_105 0.262216690 0.8362421
## Brevundimonas_126 0.213723991 0.8362421
## Reyranella_massiliensis_110 0.285559377 0.8362421
## Noviherbaspirillum_suwonense_27 0.134044967 0.8362421
## Microbacteriaceae_71 0.008470097 0.3318072
## Dolosigranulum_pigrum_30 0.166087460 0.8362421
## Bdellovibrio_194 0.496303053 0.8362421
## Bradyrhizobium_24 0.305751755 0.8362421
## Stenotrophomonas_129 0.002798817 0.3318072
## Brevundimonas_28 0.224062033 0.8362421
## Corynebacterium_62 0.538548677 0.8362421
## Delftia_21 0.403309928 0.8362421
## Asticcacaulis_excentricus_117 0.456550997 0.8362421
## Methylobacterium_Methylorubrum_22 0.234629195 0.8362421
## Bosea_vestrisii_39 0.209821052 0.8362421
## Streptococcus_69 0.210204825 0.8362421
## Methylobacterium_Methylorubrum_61 0.232699575 0.8362421
## Meiothermus_silvanus_16 0.157126790 0.8362421
## Aquabacterium_52 0.356667362 0.8362421
## Gammaproteobacteria_226 0.009339858 0.3318072
## Nitriliruptoraceae_18 0.216621687 0.8362421
## Mesorhizobium_134 0.130952056 0.8362421
## Brevundimonas_53 0.570550443 0.8362421
## Candidatus_Paracaedibacter_150 0.065183330 0.6938212
## Brevundimonas_74 0.269941644 0.8362421
## Phenylobacterium_muchangponense_40 0.384958590 0.8362421
## Afipia_72 0.289004896 0.8362421
## Sphingopyxis_48 0.527072449 0.8362421
## Thermus_3 0.366629720 0.8362421
## Corynebacterium_59 0.251268955 0.8362421
## Rhodopseudomonas_41 0.380740980 0.8362421
## Methylobacterium_Methylorubrum_26 0.387599369 0.8362421
## Sphingomonas_koreensis_42 0.481795784 0.8362421
## Fluviicola_76 0.345727981 0.8362421
## Staphylococcus_51 0.276851165 0.8362421
## Cupriavidus_gilardii_67 0.418156593 0.8362421
## Klebsiella_245 0.062398792 0.6938212
## Bosea_73 0.420468117 0.8362421
## Bacteriovorax_stolpii_38 0.537019998 0.8362421
## Thermus_thermophilus_10 0.373553545 0.8362421
## Pseudomonas_japonica_45 0.393184759 0.8362421
## Acinetobacter_100 0.263575084 0.8362421
## Mesorhizobium_294 0.066446888 0.6938212
## Granulicatella_167 0.068711030 0.6938212
## Streptococcus_216 0.071249684 0.6938212
## Sphingomonas_141 0.284654709 0.8362421
## Curvibacter_2 0.358266406 0.8362421
## Enhydrobacter_70 0.341841509 0.8362421
## Paucibacter_151 0.485255721 0.8362421
## Burkholderia_Caballeronia_Paraburkholderia_55 0.776878643 0.8880824
## Acinetobacter_88 0.127874583 0.8362421
## Methylobacillus_98 0.394631917 0.8362421
## Phreatobacter_oligotrophus_186 0.271629089 0.8362421
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_153 0.488852460 0.8362421
## Brevundimonas_124 0.362977022 0.8362421
## Providencia_130 0.242019180 0.8362421
## Tepidiphilus_232 0.316312248 0.8362421
## Streptococcus_90 0.494629901 0.8362421
## Sphingomonas_184 0.440784497 0.8362421
## Escherichia/Shigella_93 0.449556909 0.8362421
## Methylobacterium_Methylorubrum_32 0.638605399 0.8362421
## Brevundimonas_149 0.493311528 0.8362421
## Legionella_lytica_244 0.303520688 0.8362421
## Gemella_213 0.082952689 0.6938212
## Sphingobium_137 0.491674641 0.8362421
## Nocardioides_95 0.524589019 0.8362421
## Brachymonas_75 0.571786339 0.8362421
## Ottowia_14 0.577697005 0.8362421
## PMMR1_132 0.530076291 0.8362421
## Pseudomonas_12 0.564422986 0.8362421
## Caulobacteraceae_182 0.365732610 0.8362421
## Psychroglaciecola_87 0.486261777 0.8362421
## Phenylobacterium_mobile_101 0.466184237 0.8362421
## Novosphingobium_311 0.484252693 0.8362421
## Alphaproteobacteria_187 0.386094601 0.8362421
## Haliangium_107 0.492011626 0.8362421
## Pseudomonas_154 0.471509142 0.8362421
## Sphingobium_205 0.399139231 0.8362421
## Haemophilus_114 0.462764462 0.8362421
## Lawsonella_clevelandensis_143 0.598879397 0.8362421
## Sphingomonadaceae_84 0.569506019 0.8362421
## Caulobacter_272 0.434525295 0.8362421
## Hydrocarboniphaga_389 0.520953942 0.8362421
## Neisseria_118 0.454194852 0.8362421
## Bosea_211 0.345106320 0.8362421
## Comamonas_25 0.778184151 0.8880824
## Stenotrophomonas_282 0.511927174 0.8362421
## Caulobacter_104 0.529247298 0.8362421
## Xanthobacter_autotrophicus_29 0.676676507 0.8362421
## Haemophilus_133 0.635443559 0.8362421
## Brevundimonas_20 0.666133678 0.8362421
## Vulcaniibacterium_thermophilum_321 0.434509644 0.8362421
## Bosea_231 0.349996367 0.8362421
## Azospirillum_15 0.998699474 0.9999272
## Paracoccus_206 0.424649110 0.8362421
## Methylobacterium_Methylorubrum_265 0.403568850 0.8362421
## Caulobacter_6 0.691930326 0.8385646
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_64 0.725023726 0.8559958
## Frankiales_158 0.395070941 0.8362421
## Methyloversatilis_23 0.710054904 0.8499323
## Ralstonia_121 0.645156585 0.8362421
## Finegoldia_171 0.630282483 0.8362421
## Blastococcus_57 0.641663583 0.8362421
## Ellin6055_144 0.544039575 0.8362421
## Halomonas_94 0.658739405 0.8362421
## Massilia_111 0.670553146 0.8362421
## Brevundimonas_135 0.631119662 0.8362421
## Belnapia_rosea_259 0.658686475 0.8362421
## Limnohabitans_82 0.621601248 0.8362421
## Aquabacterium_parvum_46 0.862744857 0.9232884
## Rhodopseudomonas_155 0.644166582 0.8362421
## Tepidimonas_128 0.689307602 0.8385646
## Acinetobacter_lwoffii_175 0.538378791 0.8362421
## Anoxybacillus_127 0.613734020 0.8362421
## Streptococcus_195 0.613047904 0.8362421
## Bacillus_83 0.661643415 0.8362421
## Dietzia_109 0.568773315 0.8362421
## Sphingopyxis_122 0.746141708 0.8663046
## Novosphingobium_159 0.677413180 0.8362421
## Staphylococcus_17 0.818099227 0.9073464
## Acinetobacter_79 0.657813673 0.8362421
## DSSD61_338 0.618203773 0.8362421
## Micrococcaceae_212 0.589324158 0.8362421
## Ruminiclostridium_341 0.628772284 0.8362421
## Pyrinomonas_207 0.651904132 0.8362421
## Bosea_9 0.787498762 0.8880824
## Clostridium_sensu_stricto_1_293 0.620313144 0.8362421
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_343 0.620313144 0.8362421
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_199 0.666091542 0.8362421
## Duganella_166 0.680874705 0.8362421
## Cellulomonas_360 0.622921791 0.8362421
## Devosia_148 0.789839298 0.8880824
## Comamonadaceae_7 0.817359865 0.9073464
## Paenarthrobacter_344 0.663450823 0.8362421
## Acetobacteraceae_230 0.626239675 0.8362421
## Limnobacter_thiooxidans_236 0.624258649 0.8362421
## Dietzia_timorensis_339 0.624258649 0.8362421
## Micrococcus_97 0.791024248 0.8880824
## Thiobacillus_thioparus_49 0.889301804 0.9353002
## Stenotrophomonas_115 0.747956958 0.8663046
## Staphylococcus_264 0.564220078 0.8362421
## Rhodococcus_221 0.744403356 0.8663046
## Flavihumibacter_165 0.710599151 0.8499323
## Rhodopseudomonas_156 0.642243254 0.8362421
## Frankiales_280 0.584545158 0.8362421
## Halomonas_178 0.575348705 0.8362421
## Geodermatophilus_234 0.575348705 0.8362421
## Sphingobium_yanoikuyae_120 0.825460576 0.9099957
## Skermanella_aerolata_77 0.645604892 0.8362421
## Tepidiphilus_succinatimandens_106 0.832001806 0.9117146
## Ralstonia_66 0.878437112 0.9292138
## Schlegelella_4 0.852130391 0.9221700
## Sphingosinicella_163 0.856660632 0.9221700
## Stenotrophomonas_277 0.614496400 0.8362421
## Alcaligenes_60 0.946182630 0.9673264
## Novispirillum_220 0.849115043 0.9221700
## Brachybacterium_200 0.906091998 0.9463797
## Roseomonas_cervicalis_243 0.874013210 0.9292138
## Actinomyces_119 0.910179340 0.9463797
## Alloprevotella_219 0.723569053 0.8559958
## Cupriavidus_78 0.940232345 0.9673264
## Streptococcus_34 0.941503831 0.9673264
## Paucibacter_19 0.951592555 0.9674524
## Sphingobium_11 0.999927249 0.9999272
## Caulobacteraceae_92 0.987703998 0.9986179Differential abundance testing was instead preformed independently for each miseq run ### Run 514
metaSeq514 <- phyloseq_to_metagenomeSeq(ps_filtered514) # raw sequence count phyloseq
metaSeq514 <- filterData(metaSeq514, present = 5, depth = 2)
metaSeq_norm514 <- cumNorm(metaSeq514, p=0.5) # internal normalisation
mod514 <- model.matrix(~ Sample_type, pData(metaSeq_norm514)) # formula
fit514 <- fitFeatureModel(metaSeq_norm514, mod514) # NOT fitZig; gives higher # false-positives## Warning: Partial NA coefficients for 9 probe(s)## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredVP.df_514 <- MRcoefs(fit514, number = Inf, alpha = 1, adjustMethod = "BH") # extract all output (not only significant)
VP.df_514## logFC se
## Brevundimonas_149 -2.887065034 0.8923296
## Corynebacterium_44 -2.652899500 0.9129370
## Moraxella_37 -2.458623682 0.9275957
## Pseudomonas_154 -2.320365106 0.7588196
## Lactococcus_31 2.231038309 0.4736054
## Thermus_thermophilus_10 1.976475589 0.6293105
## Tepidimonas_1 1.875505065 0.7644720
## Pseudomonas_33 1.752112343 0.9040540
## Bosea_36 -1.600351562 0.7181944
## Sphingobium_137 -1.510598036 1.1485324
## Afipia_72 -1.472259699 0.7589254
## Thermus_5 1.357867536 0.6867630
## Phenylobacterium_mobile_101 -1.280082745 0.8877307
## Brevundimonas_kwangchunensis_65 1.274222277 0.5325175
## Staphylococcus_17 -1.250155329 0.5586200
## Burkholderia_Caballeronia_Paraburkholderia_55 -1.246685957 0.7170069
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_64 -1.221548457 0.6693315
## Sphingomonas_8 -1.197001959 0.4291661
## Methylobacterium_Methylorubrum_61 -1.196808093 0.5747841
## Methylobacterium_Methylorubrum_32 -1.123053815 0.6731775
## Paucibacter_19 -1.033531026 0.6262404
## Dolosigranulum_pigrum_30 -1.030282440 1.0591319
## Rhodopseudomonas_41 -0.942510214 0.7241649
## Delftia_21 -0.915540872 0.7117670
## Brevundimonas_74 -0.850069118 1.2252836
## Psychroglaciecola_87 -0.831011119 0.9560489
## Streptococcus_34 0.807254961 0.6340360
## Anoxybacillus_102 0.804734599 0.4503955
## Pseudarthrobacter_80 0.787997462 0.4764445
## Caulobacter_104 0.763376905 1.0694438
## Acinetobacter_79 0.753685705 0.4933171
## Methylopilaceae_176 0.672438393 0.4244378
## Neisseria_63 0.622767289 0.5087860
## Thermus_3 0.573971390 0.6114840
## Methylobacterium_Methylorubrum_13 0.530297064 0.3762302
## Nocardioides_95 -0.507305004 1.3046422
## Methylobacterium_Methylorubrum_26 0.502426270 0.8474632
## Bosea_96 0.470786619 0.4659718
## Duganella_166 0.465591503 0.4596924
## Acinetobacter_lwoffii_175 0.449789688 0.4363817
## Skermanella_aerolata_77 0.440128916 0.4603260
## Massilia_111 0.429513104 0.4751838
## Thermus_brockianus_50 0.406061325 0.5451662
## Noviherbaspirillum_suwonense_27 -0.390267675 0.4170381
## Corynebacterium_35 0.362894762 0.6423301
## Cloacibacterium_56 0.352771071 0.4923347
## Pseudomonas_12 0.350303924 0.3352417
## Enhydrobacter_70 0.330803305 0.5219021
## Blastococcus_57 0.293083465 0.5359095
## Bradyrhizobium_24 0.278440577 0.5499671
## Sphingomonas_koreensis_42 -0.268381749 0.4291382
## Escherichia/Shigella_93 0.230822828 0.4271349
## Methylobacterium_Methylorubrum_22 0.183355853 0.4372586
## Ralstonia_66 0.139274234 1.1171454
## Rhodopseudomonas_155 0.131521547 0.4565683
## Nitriliruptoraceae_18 0.125022071 0.5611269
## Streptococcus_90 -0.085094695 0.4960893
## Haemophilus_114 -0.065618167 0.4474498
## Corynebacterium_62 0.055044801 0.9611847
## Pseudomonas_japonica_45 -0.046402468 0.6578395
## Halomonas_94 0.031696015 1.1879194
## Micrococcus_97 -0.006514552 0.4698287
## pvalues adjPvalues
## Brevundimonas_149 1.214621e-03 0.0345530232
## Corynebacterium_44 3.662036e-03 0.0454092518
## Moraxella_37 8.036470e-03 0.0711801585
## Pseudomonas_154 2.229227e-03 0.0345530232
## Lactococcus_31 2.468027e-06 0.0001530177
## Thermus_thermophilus_10 1.685446e-03 0.0345530232
## Tepidimonas_1 1.415390e-02 0.1096926883
## Pseudomonas_33 5.261571e-02 0.2174782868
## Bosea_36 2.586061e-02 0.1457597808
## Sphingobium_137 1.884286e-01 0.4338865302
## Afipia_72 5.238860e-02 0.2174782868
## Thermus_5 4.801906e-02 0.2174782868
## Phenylobacterium_mobile_101 1.493103e-01 0.3857182126
## Brevundimonas_kwangchunensis_65 1.671911e-02 0.1151761202
## Staphylococcus_17 2.522527e-02 0.1457597808
## Burkholderia_Caballeronia_Paraburkholderia_55 8.208114e-02 0.2827239250
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_64 6.799686e-02 0.2634878337
## Sphingomonas_8 5.284911e-03 0.0546107460
## Methylobacterium_Methylorubrum_61 3.732539e-02 0.1928478286
## Methylobacterium_Methylorubrum_32 9.525863e-02 0.2918912444
## Paucibacter_19 9.886639e-02 0.2918912444
## Dolosigranulum_pigrum_30 3.306720e-01 0.5700280019
## Rhodopseudomonas_41 1.930829e-01 0.4338865302
## Delftia_21 1.983408e-01 0.4338865302
## Brevundimonas_74 4.878244e-01 0.6873888630
## Psychroglaciecola_87 3.847301e-01 0.5963315892
## Streptococcus_34 2.029469e-01 0.4338865302
## Anoxybacillus_102 7.398132e-02 0.2698142396
## Pseudarthrobacter_80 9.814529e-02 0.2918912444
## Caulobacter_104 4.753463e-01 0.6853830187
## Acinetobacter_79 1.265643e-01 0.3411733001
## Methylopilaceae_176 1.131246e-01 0.3188057190
## Neisseria_63 2.209425e-01 0.4566144236
## Thermus_3 3.479088e-01 0.5700280019
## Methylobacterium_Methylorubrum_13 1.586869e-01 0.3935433931
## Nocardioides_95 6.973900e-01 0.8158147238
## Methylobacterium_Methylorubrum_26 5.532755e-01 0.7298527527
## Bosea_96 3.123359e-01 0.5695536810
## Duganella_166 3.111401e-01 0.5695536810
## Acinetobacter_lwoffii_175 3.026696e-01 0.5695536810
## Skermanella_aerolata_77 3.390094e-01 0.5700280019
## Massilia_111 3.660546e-01 0.5819329338
## Thermus_brockianus_50 4.563688e-01 0.6853830187
## Noviherbaspirillum_suwonense_27 3.493720e-01 0.5700280019
## Corynebacterium_35 5.720968e-01 0.7302640407
## Cloacibacterium_56 4.736660e-01 0.6853830187
## Pseudomonas_12 2.960556e-01 0.5695536810
## Enhydrobacter_70 5.261841e-01 0.7166532760
## Blastococcus_57 5.844544e-01 0.7302640407
## Bradyrhizobium_24 6.126560e-01 0.7447974708
## Sphingomonas_koreensis_42 5.317105e-01 0.7166532760
## Escherichia/Shigella_93 5.889226e-01 0.7302640407
## Methylobacterium_Methylorubrum_22 6.749747e-01 0.8047774842
## Ralstonia_66 9.007850e-01 0.9629081093
## Rhodopseudomonas_155 7.732967e-01 0.8878591414
## Nitriliruptoraceae_18 8.236870e-01 0.9285199014
## Streptococcus_90 8.638063e-01 0.9563569205
## Haemophilus_114 8.834089e-01 0.9609009183
## Corynebacterium_62 9.543320e-01 0.9861430441
## Pseudomonas_japonica_45 9.437657e-01 0.9861430441
## Halomonas_94 9.787134e-01 0.9889370439
## Micrococcus_97 9.889370e-01 0.9889370439print(xtable((head(MRcoefs(fit514))), digits = 4, type = "latex"), file = "DAT_514.tex")7.2.4 Run 523
metaSeq523 <- phyloseq_to_metagenomeSeq(ps_filtered523) # raw sequence count phyloseq
metaSeq514 <- filterData(metaSeq514, present = 5, depth = 1)
metaSeq_norm523 <- cumNorm(metaSeq523, p=0.5) # internal normalisation
mod523 <- model.matrix(~ Sample_type, pData(metaSeq_norm523)) # formula
fit523 <- fitFeatureModel(metaSeq_norm523, mod523) # NOT fitZig; gives higher # false-positives## Warning: Partial NA coefficients for 54 probe(s)## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredVP.df_523 <- MRcoefs(fit523, number = Inf, alpha = 1, adjustMethod = "BH") # extract all output (not only significant)
VP.df_523## logFC se
## Cloacibacterium_56 2.78494211 0.8598828
## Vulcaniibacterium_139 2.22467997 0.9433707
## Corynebacterium_35 1.64192849 0.5296951
## Moraxella_37 1.62900941 0.5557890
## Pseudomonas_86 1.30417727 0.7416231
## Blastomonas_131 1.17118991 0.9929321
## Microbacteriaceae_71 1.12492259 0.4413106
## Aquabacterium_47 1.00460681 0.6406654
## Dolosigranulum_pigrum_30 0.99152671 0.4597552
## Pseudomonas_33 0.98863103 0.7820414
## Meiothermus_silvanus_16 -0.97556696 0.5917932
## Sphingomonas_koreensis_116 0.91543848 0.8394389
## Corynebacterium_62 0.89713131 0.3488546
## Burkholderia_Caballeronia_Paraburkholderia_55 0.85536742 0.3525797
## Stenotrophomonas_129 0.83216311 0.3513700
## Bosea_vestrisii_39 -0.80212122 0.6363700
## Bosea_36 -0.79777386 0.5559096
## Gammaproteobacteria_226 0.76009597 0.3390865
## Proteus_89 0.74875518 0.7483807
## Pseudomonas_105 0.73935830 0.8852611
## Bosea_73 -0.72982836 0.5961619
## Reyranella_massiliensis_110 0.69461685 0.7600670
## Corynebacterium_44 -0.66751261 0.4339151
## Candidatus_Paracaedibacter_150 0.66531702 0.4176313
## Fluviicola_76 -0.65962805 0.5468466
## Phenylobacterium_muchangponense_40 -0.64080727 0.4453454
## Corynebacterium_59 0.63597252 0.5250761
## Cupriavidus_gilardii_67 -0.62599755 0.6044158
## PMMR1_132 -0.61130334 0.5208479
## Brevundimonas_20 -0.60038947 0.5910282
## Azospirillum_15 -0.59212002 0.5822626
## Staphylococcus_51 -0.58437275 0.4772654
## Achromobacter_43 0.57761833 0.3956690
## Thermus_3 -0.56610483 0.5772128
## Methylobacillus_98 -0.54238799 0.4871900
## Tepidiphilus_232 -0.52474126 0.3947589
## Comamonadaceae_7 -0.52090284 0.5880446
## Schlegelella_4 -0.47861219 0.5683276
## Caulobacter_272 -0.44567287 0.3789973
## Ralstonia_121 0.44054126 0.2612686
## Lawsonella_clevelandensis_143 0.42777156 0.3100431
## Streptococcus_69 -0.42557051 0.4119235
## Phreatobacter_oligotrophus_186 -0.41902901 0.3856727
## Sphingobium_11 -0.41816359 0.5703861
## Brevundimonas_126 0.41770570 0.6957438
## Brevundimonas_74 -0.41572191 0.4268358
## Asticcacaulis_excentricus_117 0.41548064 0.7735011
## Legionella_lytica_244 -0.41423244 0.3558605
## Brevundimonas_28 0.41228079 0.4568274
## Ralstonia_66 0.40510627 0.3228902
## Brachymonas_75 -0.40363821 0.5805732
## Klebsiella_245 0.40354059 0.2971795
## Granulicatella_167 0.40289010 0.2811596
## Vulcaniibacterium_thermophilum_321 -0.40205319 0.3510024
## Paucibacter_19 0.39172763 0.5949340
## Mesorhizobium_294 0.38977467 0.2850835
## Methylobacterium_Methylorubrum_13 -0.38851210 0.3870278
## Streptococcus_216 0.38846889 0.2743400
## Aquabacterium_52 0.38724387 0.5343417
## Brevundimonas_124 0.38045110 0.4431169
## Curvibacter_2 -0.37351655 0.5745202
## Caulobacter_6 -0.37083362 0.5782050
## Acinetobacter_100 -0.35596277 0.4303058
## Sphingomonadaceae_84 -0.35581095 0.5192413
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_64 0.35433873 0.2303195
## Bosea_9 -0.35081976 0.5701964
## Sphingomonas_141 0.32116634 0.3174369
## Massilia_111 0.32026232 0.3353061
## Alphaproteobacteria_187 0.31716053 0.3753429
## Mesorhizobium_134 0.31208338 0.4043428
## DSSD61_338 -0.30629551 0.3203542
## Acinetobacter_88 0.30450942 0.3049596
## Haliangium_107 -0.30418302 0.4436905
## Methyloversatilis_23 -0.29898092 0.5843372
## Sphingosinicella_163 -0.28026921 0.4944818
## Blastococcus_57 0.27729132 0.3232521
## Comamonas_25 -0.26871599 0.6167498
## Methylobacterium_Methylorubrum_265 -0.26330012 0.3185052
## Methylobacterium_Methylorubrum_32 0.26322039 0.2252886
## Paracoccus_206 0.25977015 0.3388373
## Acinetobacter_lwoffii_175 0.25832558 0.2175327
## Haemophilus_114 -0.25781827 0.3181051
## Limnobacter_thiooxidans_236 -0.25773579 0.2892132
## Dietzia_timorensis_339 -0.25773579 0.2892132
## Stenotrophomonas_115 0.25647969 0.4029119
## Caulobacteraceae_182 -0.25422695 0.3679042
## Meiothermus_58 0.25407361 0.8549798
## Flavihumibacter_165 0.24028947 0.3374419
## Brachybacterium_200 0.22945425 0.2135061
## Alcaligenes_60 -0.22830275 0.7137851
## Cupriavidus_78 -0.22809320 0.6262700
## Sphingopyxis_48 0.21997923 0.6154686
## Skermanella_aerolata_77 -0.21592126 0.2523120
## Devosia_148 -0.21356592 0.5212149
## Micrococcaceae_212 -0.20745655 0.3064510
## Sphingomonas_koreensis_42 0.20237902 0.4493712
## Sphingomonas_184 -0.20090915 0.3415090
## Pseudomonas_12 -0.19815220 0.5801575
## Gemella_213 0.19634746 0.2311363
## Providencia_130 -0.17685338 0.3344940
## Enhydrobacter_70 -0.17485346 0.2996333
## Xanthobacter_autotrophicus_29 0.17333843 0.7500227
## Ellin6055_144 -0.17231355 0.3540088
## Dietzia_109 -0.17194963 0.3108814
## Frankiales_158 -0.16618705 0.2851664
## Pyrinomonas_207 0.16401343 0.3520479
## Tepidimonas_128 -0.16241445 0.4506103
## Micrococcus_97 0.16006933 0.3638308
## Bosea_211 -0.15475324 0.3117680
## Actinomyces_119 0.14715560 0.3362972
## Bosea_231 -0.14451722 0.2963716
## Streptococcus_90 -0.13811313 0.2615156
## Aquabacterium_parvum_46 -0.13232148 0.5999027
## Nitriliruptoraceae_18 0.12750874 0.4058932
## Acinetobacter_79 0.12734598 0.3568063
## Afipia_72 -0.11752140 0.2419879
## Sphingobium_yanoikuyae_120 -0.11624086 0.4855536
## Frankiales_280 -0.10693087 0.2524627
## Novispirillum_220 0.09513929 0.3586848
## Staphylococcus_17 -0.09155807 0.3802851
## Bdellovibrio_194 -0.09121743 0.9856450
## Streptococcus_34 -0.08702313 0.4532219
## Sphingobium_205 -0.08680642 0.2931058
## Escherichia/Shigella_93 -0.08073976 0.3792433
## Roseomonas_cervicalis_243 -0.08052496 0.3522336
## Brevundimonas_53 -0.07348432 0.9101201
## Delftia_21 0.07173230 0.6172384
## Staphylococcus_264 -0.06459579 0.2424957
## Neisseria_118 -0.05980899 0.2323873
## Halomonas_178 -0.05980899 0.2323873
## Geodermatophilus_234 -0.05980899 0.2323873
## Thiobacillus_thioparus_49 -0.05158705 0.5022265
## Stenotrophomonas_277 -0.04758952 0.2083868
## Bacteriovorax_stolpii_38 0.04666569 0.4304405
## Bradyrhizobium_24 -0.04238205 0.4586196
## Sphingopyxis_122 0.03910748 0.5056904
## Tepidiphilus_succinatimandens_106 -0.03374016 0.4866658
## Rhodococcus_221 -0.03066735 0.3883301
## Alloprevotella_219 -0.02840283 0.1779659
## Ottowia_14 0.02387621 0.3309215
## Caulobacteraceae_92 0.01619369 0.4943984
## Halomonas_94 0.01446747 0.3370576
## pvalues adjPvalues
## Cloacibacterium_56 0.001200564 0.1375103
## Vulcaniibacterium_139 0.018362584 0.3259359
## Corynebacterium_35 0.001936764 0.1375103
## Moraxella_37 0.003378885 0.1599339
## Pseudomonas_86 0.078654913 0.8285124
## Blastomonas_131 0.238188520 0.8285124
## Microbacteriaceae_71 0.010801690 0.3067680
## Aquabacterium_47 0.116865285 0.8285124
## Dolosigranulum_pigrum_30 0.031033690 0.4406784
## Pseudomonas_33 0.206170031 0.8285124
## Meiothermus_silvanus_16 0.099251539 0.8285124
## Sphingomonas_koreensis_116 0.275477029 0.8285124
## Corynebacterium_62 0.010121558 0.3067680
## Burkholderia_Caballeronia_Paraburkholderia_55 0.015265201 0.3259359
## Stenotrophomonas_129 0.017868186 0.3259359
## Bosea_vestrisii_39 0.207502197 0.8285124
## Bosea_36 0.151264746 0.8285124
## Gammaproteobacteria_226 0.024987297 0.3942440
## Proteus_89 0.317068408 0.8285124
## Pseudomonas_105 0.403612618 0.8285124
## Bosea_73 0.220872362 0.8285124
## Reyranella_massiliensis_110 0.360775214 0.8285124
## Corynebacterium_44 0.123963468 0.8285124
## Candidatus_Paracaedibacter_150 0.111143885 0.8285124
## Fluviicola_76 0.227725136 0.8285124
## Phenylobacterium_muchangponense_40 0.150178962 0.8285124
## Corynebacterium_59 0.225818488 0.8285124
## Cupriavidus_gilardii_67 0.300338918 0.8285124
## PMMR1_132 0.240527366 0.8285124
## Brevundimonas_20 0.309706048 0.8285124
## Azospirillum_15 0.309186934 0.8285124
## Staphylococcus_51 0.220794221 0.8285124
## Achromobacter_43 0.144330612 0.8285124
## Thermus_3 0.326713174 0.8285124
## Methylobacillus_98 0.265580145 0.8285124
## Tepidiphilus_232 0.183758814 0.8285124
## Comamonadaceae_7 0.375713424 0.8285124
## Schlegelella_4 0.399708795 0.8285124
## Caulobacter_272 0.239624385 0.8285124
## Ralstonia_121 0.091764495 0.8285124
## Lawsonella_clevelandensis_143 0.167673930 0.8285124
## Streptococcus_69 0.301543118 0.8285124
## Phreatobacter_oligotrophus_186 0.277262835 0.8285124
## Sphingobium_11 0.463482971 0.8737878
## Brevundimonas_126 0.548257784 0.8836291
## Brevundimonas_74 0.330075312 0.8285124
## Asticcacaulis_excentricus_117 0.591168888 0.8906458
## Legionella_lytica_244 0.244411711 0.8285124
## Brevundimonas_28 0.366798217 0.8285124
## Ralstonia_66 0.209614743 0.8285124
## Brachymonas_75 0.486904354 0.8737878
## Klebsiella_245 0.174494763 0.8285124
## Granulicatella_167 0.151869684 0.8285124
## Vulcaniibacterium_thermophilum_321 0.252025799 0.8285124
## Paucibacter_19 0.510256187 0.8760700
## Mesorhizobium_294 0.171553223 0.8285124
## Methylobacterium_Methylorubrum_13 0.315458143 0.8285124
## Streptococcus_216 0.156771841 0.8285124
## Aquabacterium_52 0.468628686 0.8737878
## Brevundimonas_124 0.390572579 0.8285124
## Curvibacter_2 0.515604067 0.8760700
## Caulobacter_6 0.521293246 0.8760700
## Acinetobacter_100 0.408105519 0.8285124
## Sphingomonadaceae_84 0.493185140 0.8737878
## Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium_64 0.123934777 0.8285124
## Bosea_9 0.538382186 0.8836291
## Sphingomonas_141 0.311658196 0.8285124
## Massilia_111 0.339509834 0.8285124
## Alphaproteobacteria_187 0.398117098 0.8285124
## Mesorhizobium_134 0.440215865 0.8622877
## DSSD61_338 0.339014038 0.8285124
## Acinetobacter_88 0.318025343 0.8285124
## Haliangium_107 0.492981255 0.8737878
## Methyloversatilis_23 0.608890248 0.8906458
## Sphingosinicella_163 0.570854317 0.8906458
## Blastococcus_57 0.390993170 0.8285124
## Comamonas_25 0.663056641 0.8967052
## Methylobacterium_Methylorubrum_265 0.408421588 0.8285124
## Methylobacterium_Methylorubrum_32 0.242657738 0.8285124
## Paracoccus_206 0.443288742 0.8622877
## Acinetobacter_lwoffii_175 0.235020490 0.8285124
## Haemophilus_114 0.417663508 0.8353270
## Limnobacter_thiooxidans_236 0.372842281 0.8285124
## Dietzia_timorensis_339 0.372842281 0.8285124
## Stenotrophomonas_115 0.524408073 0.8760700
## Caulobacteraceae_182 0.489556739 0.8737878
## Meiothermus_58 0.766337367 0.9223425
## Flavihumibacter_165 0.476408246 0.8737878
## Brachybacterium_200 0.282510545 0.8285124
## Alcaligenes_60 0.749083528 0.9223425
## Cupriavidus_78 0.715701899 0.9223425
## Sphingopyxis_48 0.720779310 0.9223425
## Skermanella_aerolata_77 0.392124560 0.8285124
## Devosia_148 0.681992013 0.9050735
## Micrococcaceae_212 0.498428249 0.8737878
## Sphingomonas_koreensis_42 0.652450568 0.8967052
## Sphingomonas_184 0.556332164 0.8836291
## Pseudomonas_12 0.732690326 0.9223425
## Gemella_213 0.395610030 0.8285124
## Providencia_130 0.597000287 0.8906458
## Enhydrobacter_70 0.559517597 0.8836291
## Xanthobacter_autotrophicus_29 0.817228639 0.9223425
## Ellin6055_144 0.626435931 0.8906458
## Dietzia_109 0.580192518 0.8906458
## Frankiales_158 0.560046643 0.8836291
## Pyrinomonas_207 0.641298657 0.8967052
## Tepidimonas_128 0.718523943 0.9223425
## Micrococcus_97 0.659969386 0.8967052
## Bosea_211 0.619631230 0.8906458
## Actinomyces_119 0.661693617 0.8967052
## Bosea_231 0.625817820 0.8906458
## Streptococcus_90 0.597411985 0.8906458
## Aquabacterium_parvum_46 0.825426042 0.9223425
## Nitriliruptoraceae_18 0.753412001 0.9223425
## Acinetobacter_79 0.721162837 0.9223425
## Afipia_72 0.627215381 0.8906458
## Sphingobium_yanoikuyae_120 0.810796496 0.9223425
## Frankiales_280 0.671893179 0.9000833
## Novispirillum_220 0.790820881 0.9223425
## Staphylococcus_17 0.809739854 0.9223425
## Bdellovibrio_194 0.926264299 0.9582237
## Streptococcus_34 0.847734378 0.9331650
## Sphingobium_205 0.767107330 0.9223425
## Escherichia/Shigella_93 0.831407302 0.9223425
## Roseomonas_cervicalis_243 0.819170231 0.9223425
## Brevundimonas_53 0.935647658 0.9582237
## Delftia_21 0.907482226 0.9582237
## Staphylococcus_264 0.789947237 0.9223425
## Neisseria_118 0.796894918 0.9223425
## Halomonas_178 0.796894918 0.9223425
## Geodermatophilus_234 0.796894918 0.9223425
## Thiobacillus_thioparus_49 0.918187826 0.9582237
## Stenotrophomonas_277 0.819357776 0.9223425
## Bacteriovorax_stolpii_38 0.913667462 0.9582237
## Bradyrhizobium_24 0.926370548 0.9582237
## Sphingopyxis_122 0.938357179 0.9582237
## Tepidiphilus_succinatimandens_106 0.944727573 0.9582237
## Rhodococcus_221 0.937054588 0.9582237
## Alloprevotella_219 0.873198526 0.9538015
## Ottowia_14 0.942481999 0.9582237
## Caulobacteraceae_92 0.973870488 0.9738705
## Halomonas_94 0.965763052 0.97261247.2.5 Top 20 Taxa
7.2.5.1 Run 514
# Select results of top 20 most abundant taxa in run 514
top20_514_names <- as.vector(mean_ab_groups_str_514$ASV)
VP.df_top20_514 <- subset(VP.df_514, row.names(VP.df_514) %in% top20_514_names)
VP.df_top20_514## logFC se pvalues
## Lactococcus_31 2.2310383 0.4736054 2.468027e-06
## Thermus_thermophilus_10 1.9764756 0.6293105 1.685446e-03
## Tepidimonas_1 1.8755051 0.7644720 1.415390e-02
## Thermus_5 1.3578675 0.6867630 4.801906e-02
## Staphylococcus_17 -1.2501553 0.5586200 2.522527e-02
## Sphingomonas_8 -1.1970020 0.4291661 5.284911e-03
## Methylobacterium_Methylorubrum_32 -1.1230538 0.6731775 9.525863e-02
## Paucibacter_19 -1.0335310 0.6262404 9.886639e-02
## Rhodopseudomonas_41 -0.9425102 0.7241649 1.930829e-01
## Delftia_21 -0.9155409 0.7117670 1.983408e-01
## Streptococcus_34 0.8072550 0.6340360 2.029469e-01
## Thermus_3 0.5739714 0.6114840 3.479088e-01
## Methylobacterium_Methylorubrum_13 0.5302971 0.3762302 1.586869e-01
## Methylobacterium_Methylorubrum_26 0.5024263 0.8474632 5.532755e-01
## Noviherbaspirillum_suwonense_27 -0.3902677 0.4170381 3.493720e-01
## Corynebacterium_35 0.3628948 0.6423301 5.720968e-01
## Pseudomonas_12 0.3503039 0.3352417 2.960556e-01
## Bradyrhizobium_24 0.2784406 0.5499671 6.126560e-01
## Methylobacterium_Methylorubrum_22 0.1833559 0.4372586 6.749747e-01
## Nitriliruptoraceae_18 0.1250221 0.5611269 8.236870e-01
## adjPvalues
## Lactococcus_31 0.0001530177
## Thermus_thermophilus_10 0.0345530232
## Tepidimonas_1 0.1096926883
## Thermus_5 0.2174782868
## Staphylococcus_17 0.1457597808
## Sphingomonas_8 0.0546107460
## Methylobacterium_Methylorubrum_32 0.2918912444
## Paucibacter_19 0.2918912444
## Rhodopseudomonas_41 0.4338865302
## Delftia_21 0.4338865302
## Streptococcus_34 0.4338865302
## Thermus_3 0.5700280019
## Methylobacterium_Methylorubrum_13 0.3935433931
## Methylobacterium_Methylorubrum_26 0.7298527527
## Noviherbaspirillum_suwonense_27 0.5700280019
## Corynebacterium_35 0.7302640407
## Pseudomonas_12 0.5695536810
## Bradyrhizobium_24 0.7447974708
## Methylobacterium_Methylorubrum_22 0.8047774842
## Nitriliruptoraceae_18 0.92851990147.2.6 Run 523
# Select top 20 most abundant taxa in run 523
top20_523_names <- as.vector(mean_ab_groups_str_523$ASV)
VP.df_top20_523 <- subset(VP.df_523, row.names(VP.df_523) %in% top20_523_names)
VP.df_top20_523## logFC se pvalues adjPvalues
## Meiothermus_silvanus_16 -0.97556696 0.5917932 0.09925154 0.8285124
## Bosea_vestrisii_39 -0.80212122 0.6363700 0.20750220 0.8285124
## Brevundimonas_20 -0.60038947 0.5910282 0.30970605 0.8285124
## Azospirillum_15 -0.59212002 0.5822626 0.30918693 0.8285124
## Thermus_3 -0.56610483 0.5772128 0.32671317 0.8285124
## Comamonadaceae_7 -0.52090284 0.5880446 0.37571342 0.8285124
## Schlegelella_4 -0.47861219 0.5683276 0.39970880 0.8285124
## Sphingobium_11 -0.41816359 0.5703861 0.46348297 0.8737878
## Brevundimonas_28 0.41228079 0.4568274 0.36679822 0.8285124
## Paucibacter_19 0.39172763 0.5949340 0.51025619 0.8760700
## Curvibacter_2 -0.37351655 0.5745202 0.51560407 0.8760700
## Caulobacter_6 -0.37083362 0.5782050 0.52129325 0.8760700
## Bosea_9 -0.35081976 0.5701964 0.53838219 0.8836291
## Methyloversatilis_23 -0.29898092 0.5843372 0.60889025 0.8906458
## Comamonas_25 -0.26871599 0.6167498 0.66305664 0.8967052
## Pseudomonas_12 -0.19815220 0.5801575 0.73269033 0.9223425
## Xanthobacter_autotrophicus_29 0.17333843 0.7500227 0.81722864 0.9223425
## Staphylococcus_17 -0.09155807 0.3802851 0.80973985 0.9223425
## Bacteriovorax_stolpii_38 0.04666569 0.4304405 0.91366746 0.9582237
## Ottowia_14 0.02387621 0.3309215 0.94248200 0.95822377.2.7 Volcano Plot
# Plot Samples from run 514 as to microbes appear to be differentially abundant
EnhancedVolcano(VP.df_514,lab = rownames(VP.df_514),x = 'logFC',y = 'adjPvalues', selectLab = c("Moraxella_37","Brevundimonas_149", "Corynebacterium_44", "Lactococcus_31"), pointSize = 3.0, labSize = 5, labhjust = 0.75, pCutoff = .05, xlim = c(-7, 7), gridlines.major = TRUE, cutoffLineWidth = 0.2,cutoffLineCol = 'grey30', FCcutoff = 2)+
ggsave("VolcanoPlot_fitFeatureModel.tiff", units="in", width=8, height=6, dpi=300, compression = 'lzw')
### Boxplot of these taxa
otu_DAT2 <- select(as.data.frame(t(otu_table_ord_RA)), "Moraxella_37","Brevundimonas_149", "Corynebacterium_44", "Lactococcus_31")
otu_DAT2 <- t(otu_DAT2)
boxplot.dfT_DAT2 <- cbind(data.frame(Sample.ID=rownames(sample_data(ps_filtered))), t(otu_DAT2) %>%
data.frame(check.names = F)) %>%
gather(key=ASV, value=RA, -Sample.ID) %>%
mutate(ASV = factor(ASV, levels = rev(unique(ASV))))
boxplot.dfT_DAT2$Sample_group<-Sample_group[match(boxplot.dfT_DAT2$Sample.ID, names(Sample_group))]
boxplot.dfT_DAT2 <- boxplot.dfT_DAT2 %>%
mutate(RA_percentage=RA*100)%>%
mutate(log10_RA=log10(RA_percentage+1))
pd = position_dodge(width = 0.5)
ggplot(data = boxplot.dfT_DAT2, aes(x=ASV, y=log10_RA, fill=Sample_group))+
stat_boxplot(geom = "errorbar", position = pd, width = 0.25)+
geom_boxplot(width = 0.5, position = pd)+
scale_fill_manual(values = wes_palette(n=3, name = "FantasticFox1"))+
ylab("Log10(Relative abundance)")+
xlab("ASV")+
labs(fill= "Sample Type")+
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5), legend.position = "right") +
#ggtitle("Relative Abunance of Differentially Abundant ASVs
#from Miseq Run No. 514")
theme_bw()+
ggsave("Relative_abundances_Boxplot_DiffAbundance514.pdf", units="in", width=8, height=3, dpi=300)
7.2.8 Relative Abundance Table
DAT_RA.df <- boxplot.dfT_DAT2 %>%
group_by(ASV, Sample_group) %>%
summarise_each(funs(mean)) %>%
select(ASV, Sample_group, RA_percentage, log10_RA)## Warning in mean.default(Sample.ID): argument is not numeric or logical:
## returning NA
## Warning in mean.default(Sample.ID): argument is not numeric or logical:
## returning NA
## Warning in mean.default(Sample.ID): argument is not numeric or logical:
## returning NA
## Warning in mean.default(Sample.ID): argument is not numeric or logical:
## returning NA
## Warning in mean.default(Sample.ID): argument is not numeric or logical:
## returning NA
## Warning in mean.default(Sample.ID): argument is not numeric or logical:
## returning NA
## Warning in mean.default(Sample.ID): argument is not numeric or logical:
## returning NA
## Warning in mean.default(Sample.ID): argument is not numeric or logical:
## returning NA
## Warning in mean.default(Sample.ID): argument is not numeric or logical:
## returning NA
## Warning in mean.default(Sample.ID): argument is not numeric or logical:
## returning NA
## Warning in mean.default(Sample.ID): argument is not numeric or logical:
## returning NA
## Warning in mean.default(Sample.ID): argument is not numeric or logical:
## returning NAprint(xtable(DAT_RA.df, digits = 3, type = "latex"), include.rownames=FALSE, file = "DAT_RA.tex")7.2.9 Heatmap of Differentially abundant taxa
ps_DAT2 <- subset_taxa(ps_filtered, ASV=="Corynebacterium_44"|ASV=="Lactococcus_31"|ASV=="Moraxella_37"|ASV=="Brevundimonas_149"|ASV=="Pseudomonas_12"|ASV=="Brevundimonas_20")
ps_DAT2 <- prune_samples(sample_sums(ps_DAT2)>0, ps_DAT2)
plot_heatmap(ps_DAT2, "NMDS", "bray", "Sample_type", low="#4ada85", high="#4f5dd1", na.value="white", sample.order = "Sample_type")+
labs(x="Sample_type", y="ASV")+
theme_bw()+
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5), legend.position = "bottom")## Warning: Transformation introduced infinite values in discrete y-axis
ggsave("Abundance_Heatmap_514.pdf")## Warning: Transformation introduced infinite values in discrete y-axisplot_heatmap(ps_filtered, "NMDS", "bray", "Sample_type", "Family", low="#4ada85", high="#4f5dd1", na.value="white", sample.order = "Sample_type")+
labs(x="Sample_type", y="Phyla")+
theme_bw()+
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5), legend.position = "bottom")## Warning: Transformation introduced infinite values in discrete y-axis
#ggsave("Abundance_Heatmap_514.pdf")7.2.10 Prevalence of Lactococcus
# Absolute abundance of Lactococcus
otu_Lac <- as.data.frame(t(otu_table(ps_filtered)))%>%
select(Lactococcus_31)
otu_Lac <- t(otu_Lac)
barplot.dfT_Lac <- cbind(data.frame(Sample.ID=rownames(sample_data(ps_filtered))), t(otu_Lac) %>%
data.frame(check.names = F)) %>%
gather(key=ASV, value=Absolute_Abund, -Sample.ID) %>%
mutate(ASV = factor(ASV, levels = rev(unique(ASV))))
barplot.dfT_Lac$Sample_group<-Sample_group[match(barplot.dfT_Lac$Sample.ID, names(Sample_group))]
barplot.dfT_Lac$Extraction_Date<-metadata_filtered$Extraction_date
barplot.dfT_Lac$Extraction_No<-metadata_filtered$Extracno
barplot.dfT_Lac <- barplot.dfT_Lac %>%
mutate(Sample_name = fct_reorder(Sample.ID, Extraction_Date))
ggplot(data = barplot.dfT_Lac, aes(x=Extraction_Date, y=Absolute_Abund, fill = Sample_group))+
geom_bar(stat = "identity")+
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5), legend.position = "bottom")+
ylab("Absolute Abundance")+
xlab("Sample")
Not properly arranged by date.
7.2.11 Abundance over time
barplot.dfT_Lac2 <- barplot.dfT_Lac %>%
filter(Sample_group=="stgg") %>%
arrange(Extraction_Date) %>%
mutate(Sample_name = fct_reorder(Sample.ID, Extraction_Date))
ggplot(data = barplot.dfT_Lac2, aes(x=Sample_name, y=Absolute_Abund, fill = Sample_group))+
geom_bar(stat = "identity")+
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5), legend.position = "bottom")+
ylab("Absolute Abundance")+
xlab("Sample")+
ggtitle("Absolute Abundance of Lactococcus_31 Arranged by Extraction Date")
### Abundance in each extraction number
ggplot(data = barplot.dfT_Lac2, aes(x=Sample_name, y=Absolute_Abund, fill = as.factor(Extraction_No)))+
geom_bar(stat = "identity")+
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5), legend.position = "bottom")+
ylab("Absolute Abundance")+
xlab("Sample")+
scale_fill_discrete(name = "Extraction No.")+
ggsave("STGG_samplesbydate.pdf", units="in", width=8, height=5, dpi=300)
#### Mean Absolute Abundance
mean(barplot.dfT_Lac2$Absolute_Abund)## [1] 26.557.2.11.1 Mean Relative Abundance
# Filter Lactococcus_31
Lac.df <- filter(boxplot.dfT_DAT2, ASV=="Lactococcus_31")
mean(Lac.df$RA_percentage)## [1] 0.4620817# What percentage of samples contained Lactococcus_31
Lac.df$Lac_present <- Lac.df$RA>0
sum(Lac.df$Lac_present)/nrow(Lac.df)*100## [1] 32.07547sum(Lac.df$Lac_present)## [1] 17sum(Lac.df$Lac_present)/nrow(boxplot.dfT_DAT2)*100## [1] 8.018868Lac.df.prev = Lac.df %>%
count(Sample_group, Lac_present) %>%
filter(Lac_present==TRUE)
Sample_type_N <- Lac.df %>%
count(Sample_group)%>%
rename(Total=n)%>%
filter(grepl('stgg', Sample_group))
Lac.df.prev$Total <- Sample_type_N$Total
Lac.df.prev = Lac.df %>%
count(Sample_group, Lac_present) %>%
filter(Lac_present==TRUE)
Lac.df.prev$Total <- "20"
Lac.df.prev <- Lac.df.prev %>%
mutate(Prevalence = (n/20)*100) %>%
mutate(Lower=as.numeric(c("21.04")))%>%
mutate(Upper=as.numeric(c("9.76")))%>%
select(Sample_group, n, Total, Prevalence, Lower, Upper)
# Bar Plot Showing prevalence of Strep
ggplot(data = Lac.df.prev, aes(x=Sample_group, y=Prevalence, fill=Sample_group))+
geom_bar(stat = "identity", color="black", position=position_dodge()) +
geom_errorbar(aes(ymin=Prevalence-Lower, ymax=Prevalence+Upper), width=.2, position=position_dodge(.9))+
scale_fill_manual(values = wes_palette(n=3, name = "FantasticFox1"))+
ylab("Prevalence (%)")+
xlab("Sample Type")+
ggtitle("Prevalence of Streptococcus in STGG, RNA Protect
and Enivornmental Control Samples")#+
#ggsave("Prevanlence_Strep_Bar.tiff", units="in", width=6, height=5, dpi=300, compression = 'lzw')8 Are ASVs from Streptococcus and Staphylococcus genus more abundant in STGG?
# Agglomerate at genus level
ps_SS_genus = tax_glom(ps_filtered, taxrank = "Genus")
#Extract OTUs at Genus level
otu_SS_genus <- ps_SS_genus%>%
otu_table%>%
as("matrix")%>%
magrittr::set_rownames(paste(tax_table(ps_SS_genus)[,"Genus"], seq(1:length(tax_table(ps_SS_genus)[,"Genus"])), sep = "_"))%>%
data.frame(.)
##Convert into RA
otu_SS_RA <- t(otu_SS_genus)/colSums(otu_SS_genus)
otu_SS_RA <- t(select(as.data.frame(otu_SS_RA), "Staphylococcus_14", "Streptococcus_25"))
boxplot.SS_RA <- cbind(data.frame(Sample.ID=rownames(metadata_filtered)), t(otu_SS_RA))%>%
data.frame(check.names = F)%>%
gather(key=Genus, value=RA, -Sample.ID) %>%
mutate(ASV = factor(Genus, levels = rev(unique(Genus))))
boxplot.SS_RA$Sample_group <- Sample_group[match(boxplot.SS_RA$Sample.ID, names(Sample_group))]
boxplot.SS_RA <- boxplot.SS_RA %>%
mutate(RA_percentage=RA*100)%>%
mutate(log10_RA=log10(RA+1))%>%
mutate(sqrt_RA=sqrt(RA))8.1 What Proportion of samples contained Staph/Strep
boxplot.SS_RA$SS_present <- boxplot.SS_RA$RA>0
sum(boxplot.SS_RA$SS_present)/nrow(boxplot.SS_RA)*100## [1] 58.4905758.5% of samples contained Staphylococcus or Streptococcus or Staphylococcus.
8.2 Prevalence of Streptococcus in each sample type
Strep <- filter(boxplot.SS_RA, ASV == "Streptococcus_25")
sum(Strep$SS_present)## [1] 25sum(Strep$SS_present)/nrow(boxplot.SS_RA)*100## [1] 23.58491Strep.prev = Strep %>%
count(Sample_group, SS_present) %>%
filter(SS_present==TRUE)
Sample_type_N <- Strep %>%
count(Sample_group)%>%
rename(Total=n)
Strep.prev$Total <- Sample_type_N$Total
Strep.prev <- Strep.prev %>%
mutate(Prevalence = (n/Total)*100) %>%
mutate(Lower=as.numeric(c("16.03", "18.86", "21.71")))%>%
mutate(Upper=as.numeric(c("19.01", "30.56", "16.88")))%>%
select(Sample_group, n, Total, Prevalence, Lower, Upper)
# Bar Plot Showing prevalence of Strep
ggplot(data = Strep.prev, aes(x=Sample_group, y=Prevalence, fill=Sample_group))+
geom_bar(stat = "identity", color="black", position=position_dodge()) +
geom_errorbar(aes(ymin=Prevalence-Lower, ymax=Prevalence+Upper), width=.2,
position=position_dodge(.9))+
scale_fill_manual(values = wes_palette(n=3, name = "FantasticFox1"))+
ylab("Prevalence (%)")+
xlab("Sample Type")+
ggtitle("Prevalence of Streptococcus in STGG, RNA Protect
and Enivornmental Control Samples")+
ggsave("Prevanlence_Strep_Bar.tiff", units="in", width=6, height=5, dpi=300, compression = 'lzw')
Prevalence, defined as the percentage of total samples from each sample type that contained Streptococcus. Streptococcus was present in 65% of STGG samples, 28.6% of RNA Protect samples and 38.5% of environmental control samples.
8.3 Prevalence of Staphylococcus in each sample type
Staph <- filter(boxplot.SS_RA, ASV == "Staphylococcus_14")
sum(Staph$SS_present)## [1] 37sum(Staph$SS_present)/nrow(boxplot.SS_RA)*100## [1] 34.90566Staph.prev = Staph %>%
count(Sample_group, SS_present) %>%
filter(SS_present==TRUE)
Staph.prev$Total <- Sample_type_N$Total
Staph.prev <- Staph.prev %>%
mutate(Prevalence = (n/Total)*100) %>%
mutate(Lower=as.numeric(c("19.16", "37.02", "21.34")))%>%
mutate(Upper=as.numeric(c("13.22", "11.72", "18.12")))%>%
select(Sample_group, n, Total, Prevalence, Lower, Upper)
# Bar Plot Showing prevalence of Staph
ggplot(data = Staph.prev, aes(x=Sample_group, y=Prevalence, fill=Sample_group))+
geom_bar(stat = "identity", color="black", position=position_dodge()) +
geom_errorbar(aes(ymin=Prevalence-Lower, ymax=Prevalence+Upper), width=.2,
position=position_dodge(.9))+
scale_fill_manual(values = wes_palette(n=3, name = "FantasticFox1"))+
ylab("Prevalence (%)")+
xlab("Sample Type")+
ggtitle("Prevalence of Staphylococcus in STGG, RNA Protect
and Enivornmental Control Samples")+
ggsave("Prevanlence_Staph_Bar.tiff", units="in", width=6, height=5, dpi=300, compression = 'lzw')
## Relative Abundance of Staphylococcus and Stretococcus
#boxplot.SS_RA_T <- filter(boxplot.SS_RA, SS_present==TRUE)
hist(boxplot.SS_RA$RA_percentage)
hist(boxplot.SS_RA$log10_RA)
ggplot(data = boxplot.SS_RA, aes(x=ASV, y=log10_RA, fill=Sample_group))+
stat_boxplot(geom = "errorbar", position = pd, width = 0.25)+
geom_boxplot(width = 0.5, position = pd)+
scale_fill_manual(values = wes_palette(n=3, name = "FantasticFox1"))+
#geom_dotplot(binaxis = 'y', stackdir='center', dotsize = 1)+
#geom_jitter(shape=16, position=position_jitter(0.2))+
ggtitle("Relative Abundance of Streptococcus and Staphylococcus
in STGG, RNA protect and Environmental Controls")+
ylab("log10(Relative abundance)")+
xlab("ASV")+
#theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5), legend.position = "bottom") +
ggsave("Relative_abundances_Boxplot_SS_RA.tiff", units="in", width=6, height=4, dpi=300, compression = 'lzw')
## Check Statistical Significance
Strep_T <- filter(Strep, SS_present==TRUE)
library(pscl)
shapiro.test(Strep$log10_RA)##
## Shapiro-Wilk normality test
##
## data: Strep$log10_RA
## W = 0.39633, p-value = 1.952e-13ss1 <- glm(log10_RA ~ Sample_group, family = poisson, Strep)## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.065752## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.061572## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.026170## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.004942## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.010290## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.020370## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.004466## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.001945## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.003771## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.007126## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.004203## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.001848## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.005816## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.005741## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.001269## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.003208## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.002308## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.002367## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.000691## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.000939
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.000939## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.006214## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.000510## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.001190## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.002555plot(ss1)
summary(ss1)##
## Call:
## glm(formula = log10_RA ~ Sample_group, family = poisson, data = Strep)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.11439 -0.11439 -0.08507 -0.00409 0.43017
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -5.0295 2.4247 -2.074 0.0381 *
## Sample_grouprna_protect -2.5038 16.5193 -0.152 0.8795
## Sample_groupstgg -0.5923 4.4383 -0.133 0.8938
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 0.81035 on 52 degrees of freedom
## Residual deviance: 0.74622 on 50 degrees of freedom
## AIC: Inf
##
## Number of Fisher Scoring iterations: 9Staph_T <- filter(Staph, SS_present==TRUE)
ss2 <- zeroinfl(log10_RA ~ Sample_group, family = poisson, Staph)## Error in zeroinfl(log10_RA ~ Sample_group, family = poisson, Staph): invalid dependent variable, non-integer valuesplot(ss2)## Error in plot(ss2): object 'ss2' not foundsummary(ss2)## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'ss2' not foundss3 <- aov(log10_RA ~ Sample_group+ASV, boxplot.SS_RA_T)## Error in terms.formula(formula, "Error", data = data): object 'boxplot.SS_RA_T' not foundss3## Error in eval(expr, envir, enclos): object 'ss3' not foundRelative abundance could not be transformed to normal distribution so Kruskal-Wallis test (non-parametric) was used to determine the statistical significance of any difference in RA between sample types. No significant difference was observed between
9 Staph/Strep ASVs
OTU9<- as(otu_table(ps_filtered), "matrix")
OTU9 <- OTU9[rowSums(t(t(OTU8)/colSums(OTU9))>=0.001)>1, ]## Warning in t(OTU8)/colSums(OTU9): longer object length is not a multiple of
## shorter object lengthotu_table_ord9<-OTU9[order(-rowSums(OTU9)), ]
otu_table_ord_RA9 <- t(t(otu_table_ord9)/colSums(otu_table_ord9))
otu_table_ord_RA9 <- as.data.frame(otu_table_ord_RA9)
otu_SS_ASV <- filter(otu_table_ord_RA9, grepl('Staphylococcus|Streptococcus', row.names(otu_table_ord_RA9)))9.1 Prevalence
boxplot.SS_RA2 <- cbind(data.frame(Sample.ID=rownames(metadata_filtered)), t(otu_SS_ASV))%>%
data.frame(check.names = F)%>%
gather(key=ASV, value=RA, -Sample.ID) %>%
mutate(ASV = factor(ASV, levels = rev(unique(ASV))))
boxplot.SS_RA2$Sample_group <- Sample_group[match(boxplot.SS_RA2$Sample.ID, names(Sample_group))]
boxplot.SS_RA2 <- boxplot.SS_RA2 %>%
mutate(RA_percentage=RA*100)%>%
mutate(log10_RA=log10(RA+1))%>%
mutate(sqrt_RA=sqrt(RA))9.1.1 What percetage of samples contained Staph or Strep
boxplot.SS_RA2$SS_present <- boxplot.SS_RA2$RA>0
sum(boxplot.SS_RA2$SS_present)/nrow(boxplot.SS_RA2)*100 #not correct## [1] 16.037749.1.2 Staphylococcus_17
Sta17 <- filter(boxplot.SS_RA2, ASV == "Staphylococcus_17")
sum(Sta17$SS_present)## [1] 33sum(Sta17$SS_present)/nrow(boxplot.SS_RA2)*100## [1] 7.783019Sta17.prev = Sta17 %>%
count(Sample_group, SS_present) %>%
filter(SS_present==TRUE)
Sample_type_N <- Sta17 %>%
count(Sample_group)%>%
rename(Total=n)
Sta17.prev$Total <- Sample_type_N$Total
Sta17.prev$ASV <- "Staphylococcus_17"
Sta17.prev <- Sta17.prev %>%
mutate(Prevalence = (n/Total)*100) %>%
mutate(Lower=as.numeric(c("19.00846", "37.02429", "20.79")))%>%
mutate(Upper=as.numeric(c("16.03154", "11.71571", "21.62")))%>%
mutate(not_present=Total-n)%>%
select(Sample_group, n, not_present, Total, Prevalence, Lower, Upper, ASV)
# Is prevalence of each sample group independent or related
sta17.prop <- prop.test(x=Sta17.prev$n, n=Sta17.prev$Total, conf.level = 0.95, correct = FALSE)## Warning in prop.test(x = Sta17.prev$n, n = Sta17.prev$Total, conf.level =
## 0.95, : Chi-squared approximation may be incorrectsta17.prop##
## 3-sample test for equality of proportions without continuity
## correction
##
## data: Sta17.prev$n out of Sta17.prev$Total
## X-squared = 2.0933, df = 2, p-value = 0.3511
## alternative hypothesis: two.sided
## sample estimates:
## prop 1 prop 2 prop 3
## 0.6153846 0.8571429 0.5500000# Bar Plot Showing prevalence of Sta17
ggplot(data = Sta17.prev, aes(x=Sample_group, y=Prevalence, fill=Sample_group))+
geom_bar(stat = "identity", color="black", position=position_dodge()) +
geom_errorbar(aes(ymin=Prevalence-Lower, ymax=Prevalence+Upper), width=.2,
position=position_dodge(.9))+
scale_fill_manual(values = wes_palette(n=3, name = "FantasticFox1"))+
ylab("Prevalence (%)")+
xlab("Sample Type")+
ggtitle("Prevalence of Streptococcus_17 in STGG, RNA Protect
and Enivornmental Control Samples")#+
#ggsave("Prevanlence_Strep_Bar.tiff", units="in", width=6, height=5, dpi=300, compression = 'lzw')9.1.3 Streptococcus_34
Str34 <- filter(boxplot.SS_RA2, ASV == "Streptococcus_34")
sum(Str34$SS_present)## [1] 13sum(Str34$SS_present)/nrow(boxplot.SS_RA2)*100## [1] 3.066038Str34.prev = Str34 %>%
count(Sample_group, SS_present) %>%
filter(SS_present==TRUE)
Sample_type_N <- Str34 %>%
count(Sample_group)%>%
rename(Total=n)
Str34.prev$Total <- Sample_type_N$Total
Str34.prev$ASV <- "Streptococcus_34"
Str34.prev <- Str34.prev %>%
mutate(Prevalence = (n/Total)*100) %>%
mutate(Lower=as.numeric(c("7.53846", "11.71571", "19.18")))%>%
mutate(Upper=as.numeric(c("17.44154", "37.02429", "20.79")))%>%
select(Sample_group, n, Total, Prevalence, Lower, Upper, ASV)
# Is prevalence of each sample group independent or related
chisq.test(Str34$Sample_group, Str34$SS_present) #ns## Warning in chisq.test(Str34$Sample_group, Str34$SS_present): Chi-squared
## approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: Str34$Sample_group and Str34$SS_present
## X-squared = 7.2944, df = 2, p-value = 0.02606str34.prop <- prop.test(x=Str34.prev$n, n=Str34.prev$Total, conf.level = 0.95, correct = FALSE)## Warning in prop.test(x = Str34.prev$n, n = Str34.prev$Total, conf.level =
## 0.95, : Chi-squared approximation may be incorrectstr34.prop##
## 3-sample test for equality of proportions without continuity
## correction
##
## data: Str34.prev$n out of Str34.prev$Total
## X-squared = 7.2944, df = 2, p-value = 0.02606
## alternative hypothesis: two.sided
## sample estimates:
## prop 1 prop 2 prop 3
## 0.1153846 0.1428571 0.4500000str34.PW <- pairwise.prop.test(x=Str34.prev$n, n=Str34.prev$Total, p.adjust.method = "BH")## Warning in prop.test(x[c(i, j)], n[c(i, j)], ...): Chi-squared approximation may
## be incorrect## Warning in prop.test(x[c(i, j)], n[c(i, j)], ...): Chi-squared approximation may
## be incorrectstr34.PW##
## Pairwise comparisons using Pairwise comparison of proportions
##
## data: Str34.prev$n out of Str34.prev$Total
##
## 1 2
## 2 1.000 -
## 3 0.079 0.481
##
## P value adjustment method: BHprint(xtable(str34.PW$p.value, type = "latex", digits = 3), file = "Str34.prev_pairwise.tex")
# Bar Plot Showing prevalence of Str34
ggplot(data = Str34.prev, aes(x=Sample_group, y=Prevalence, fill=Sample_group))+
geom_bar(stat = "identity", color="black", position=position_dodge()) +
geom_errorbar(aes(ymin=Prevalence-Lower, ymax=Prevalence+Upper), width=.2,
position=position_dodge(.9))+
scale_fill_manual(values = wes_palette(n=3, name = "FantasticFox1"))+
ylab("Prevalence (%)")+
xlab("Sample Type")+
ggtitle("Prevalence of Streptococcus_34 in STGG, RNA Protect
and Enivornmental Control Samples")#+
#ggsave("Prevanlence_Strep_Bar.tiff", units="in", width=6, height=5, dpi=300, compression = 'lzw')9.1.4 Staphylococcus_51
Sta51 <- filter(boxplot.SS_RA2, ASV == "Staphylococcus_51")
sum(Sta51$SS_present)## [1] 4sum(Sta51$SS_present)/nrow(boxplot.SS_RA2)*100## [1] 0.9433962Sta51.prev = Sta51 %>%
count(Sample_group, SS_present) %>%
filter(SS_present==TRUE)
Sample_type_N <- Sta51 %>%
count(Sample_group)%>%
rename(Total=n)%>%
filter(grepl('stgg|blank', Sample_group))
Sta51.prev$Total <- Sample_type_N$Total
Sta51.prev$ASV <- "Staphylococcus_51"
Sta51.prev <- Sta51.prev %>%
mutate(Prevalence = (n/Total)*100) %>%
mutate(Lower=as.numeric(c("7.53846", "4.11")))%>%
mutate(Upper=as.numeric(c("17.44154", "18.61")))%>%
select(Sample_group, n, Total, Prevalence, Lower, Upper, ASV)
sta51.prop <- prop.test(x=Sta51.prev$n, n=Sta51.prev$Total, conf.level = 0.95, correct = FALSE)## Warning in prop.test(x = Sta51.prev$n, n = Sta51.prev$Total, conf.level =
## 0.95, : Chi-squared approximation may be incorrectsta51.prop##
## 2-sample test for equality of proportions without continuity
## correction
##
## data: Sta51.prev$n out of Sta51.prev$Total
## X-squared = 0.6087, df = 1, p-value = 0.4353
## alternative hypothesis: two.sided
## 95 percent confidence interval:
## -0.09019268 0.22096191
## sample estimates:
## prop 1 prop 2
## 0.1153846 0.0500000# Bar Plot Showing prevalence of Sta51
ggplot(data = Sta51.prev, aes(x=Sample_group, y=Prevalence, fill=Sample_group))+
geom_bar(stat = "identity", color="black", position=position_dodge()) +
geom_errorbar(aes(ymin=Prevalence-Lower, ymax=Prevalence+Upper), width=.2,
position=position_dodge(.9))+
scale_fill_manual(values = wes_palette(n=3, name = "FantasticFox1"))+
ylab("Prevalence (%)")+
xlab("Sample Type")+
ggtitle("Prevalence of Staphylococcus_51 in STGG, RNA Protect
and Enivornmental Control Samples")#+
#ggsave("Prevanlence_Strep_Bar.tiff", units="in", width=6, height=5, dpi=300, compression = 'lzw')9.1.5 Streptococcus_69
Str69 <- filter(boxplot.SS_RA2, ASV == "Streptococcus_69")
sum(Str69$SS_present)## [1] 6sum(Str69$SS_present)/nrow(boxplot.SS_RA2)*100## [1] 1.415094Str69.prev = Str69 %>%
count(Sample_group, SS_present) %>%
filter(SS_present==TRUE)
Sample_type_N <- Str69 %>%
count(Sample_group)%>%
rename(Total=n)%>%
filter(grepl('stgg|blank', Sample_group))
Str69.prev$Total <- Sample_type_N$Total
Str69.prev$ASV <- "Streptococcus_69"
Str69.prev <- Str69.prev %>%
mutate(Prevalence = (n/Total)*100) %>%
mutate(Lower=as.numeric(c("10.72077", "4.11")))%>%
mutate(Upper=as.numeric(c("18.64923", "18.61")))%>%
select(Sample_group, n, Total, Prevalence, Lower, Upper, ASV)
str69.prop <- prop.test(x=Str69.prev$n, n=Str69.prev$Total, conf.level = 0.95, correct = FALSE)## Warning in prop.test(x = Str69.prev$n, n = Str69.prev$Total, conf.level =
## 0.95, : Chi-squared approximation may be incorrectstr69.prop##
## 2-sample test for equality of proportions without continuity
## correction
##
## data: Str69.prev$n out of Str69.prev$Total
## X-squared = 2.0184, df = 1, p-value = 0.1554
## alternative hypothesis: two.sided
## 95 percent confidence interval:
## -0.03678042 0.32139581
## sample estimates:
## prop 1 prop 2
## 0.1923077 0.0500000# Bar Plot Showing prevalence of Str69
ggplot(data = Str69.prev, aes(x=Sample_group, y=Prevalence, fill=Sample_group))+
geom_bar(stat = "identity", color="black", position=position_dodge()) +
geom_errorbar(aes(ymin=Prevalence-Lower, ymax=Prevalence+Upper), width=.2,
position=position_dodge(.9))+
scale_fill_manual(values = wes_palette(n=3, name = "FantasticFox1"))+
ylab("Prevalence (%)")+
xlab("Sample Type")+
ggtitle("Prevalence of Streptococcus_69 in STGG, RNA Protect
and Enivornmental Control Samples")#+
#ggsave("Prevanlence_Strep_Bar.tiff", units="in", width=6, height=5, dpi=300, compression = 'lzw')9.1.6 Streptococcus_90
Str90 <- filter(boxplot.SS_RA2, ASV == "Streptococcus_90")
sum(Str90$SS_present)## [1] 6sum(Str90$SS_present)/nrow(boxplot.SS_RA2)*100## [1] 1.415094Str90.prev = Str90 %>%
count(Sample_group, SS_present) %>%
filter(SS_present==TRUE)
Sample_type_N <- Str90 %>%
count(Sample_group)%>%
rename(Total=n)%>%
filter(grepl('stgg|blank', Sample_group))
Str90.prev$Total <- Sample_type_N$Total
Str90.prev$ASV <- "Streptococcus_90"
Str90.prev <- Str90.prev %>%
mutate(Prevalence = (n/Total)*100) %>%
mutate(Lower=as.numeric(c("5.552308", "11.93")))%>%
mutate(Upper=as.numeric(c("16.447692", "21.6")))%>%
select(Sample_group, n, Total, Prevalence, Lower, Upper, ASV)
str90.prop <- prop.test(x=Str90.prev$n, n=Str90.prev$Total, conf.level = 0.95, correct = FALSE)## Warning in prop.test(x = Str90.prev$n, n = Str90.prev$Total, conf.level =
## 0.95, : Chi-squared approximation may be incorrectstr90.prop##
## 2-sample test for equality of proportions without continuity
## correction
##
## data: Str90.prev$n out of Str90.prev$Total
## X-squared = 1.5097, df = 1, p-value = 0.2192
## alternative hypothesis: two.sided
## 95 percent confidence interval:
## -0.32611062 0.07995677
## sample estimates:
## prop 1 prop 2
## 0.07692308 0.20000000# Bar Plot Showing prevalence of Str90
ggplot(data = Str90.prev, aes(x=Sample_group, y=Prevalence, fill=Sample_group))+
geom_bar(stat = "identity", color="black", position=position_dodge()) +
geom_errorbar(aes(ymin=Prevalence-Lower, ymax=Prevalence+Upper), width=.2,
position=position_dodge(.9))+
scale_fill_manual(values = wes_palette(n=3, name = "FantasticFox1"))+
ylab("Prevalence (%)")+
xlab("Sample Type")+
ggtitle("Prevalence of Streptococcus_90 in STGG, RNA Protect
and Enivornmental Control Samples")#+
#ggsave("Prevanlence_Strep_Bar.tiff", units="in", width=6, height=5, dpi=300, compression = 'lzw')9.1.7 Streptococcus_195
Str195 <- filter(boxplot.SS_RA2, ASV == "Streptococcus_195")
sum(Str195$SS_present)## [1] 2sum(Str195$SS_present)/nrow(boxplot.SS_RA2)*100## [1] 0.4716981Str195.prev = Str195 %>%
count(Sample_group, SS_present) %>%
filter(SS_present==TRUE)
Sample_type_N <- Str195 %>%
count(Sample_group)%>%
rename(Total=n)%>%
filter(grepl('stgg|blank', Sample_group))
Str195.prev$Total <- Sample_type_N$Total
Str195.prev$ASV <- "Streptococcus_195"
Str195.prev <- Str195.prev %>%
mutate(Prevalence = (n/Total)*100) %>%
mutate(Lower=as.numeric(c("3.136154", "4.11")))%>%
mutate(Upper=as.numeric(c("15.693846", "18.61")))%>%
select(Sample_group, n, Total, Prevalence, Lower, Upper, ASV)
str195.prop <- prop.test(x=Str195.prev$n, n=Str195.prev$Total, conf.level = 0.95, correct = FALSE)## Warning in prop.test(x = Str195.prev$n, n = Str195.prev$Total, conf.level =
## 0.95, : Chi-squared approximation may be incorrectstr195.prop##
## 2-sample test for equality of proportions without continuity
## correction
##
## data: Str195.prev$n out of Str195.prev$Total
## X-squared = 0.036189, df = 1, p-value = 0.8491
## alternative hypothesis: two.sided
## 95 percent confidence interval:
## -0.1323173 0.1092404
## sample estimates:
## prop 1 prop 2
## 0.03846154 0.05000000# Bar Plot Showing prevalence of Str195
ggplot(data = Str195.prev, aes(x=Sample_group, y=Prevalence, fill=Sample_group))+
geom_bar(stat = "identity", color="black", position=position_dodge()) +
geom_errorbar(aes(ymin=Prevalence-Lower, ymax=Prevalence+Upper), width=.2,
position=position_dodge(.9))+
scale_fill_manual(values = wes_palette(n=3, name = "FantasticFox1"))+
ylab("Prevalence (%)")+
xlab("Sample Type")+
ggtitle("Prevalence of Streptococcus_195 in STGG, RNA Protect
and Enivornmental Control Samples")#+
#ggsave("Prevanlence_Strep_Bar.tiff", units="in", width=6, height=5, dpi=300, compression = 'lzw')9.1.8 Streptococcus_216
Str216 <- filter(boxplot.SS_RA2, ASV == "Streptococcus_216")
sum(Str216$SS_present)## [1] 2sum(Str216$SS_present)/nrow(boxplot.SS_RA2)*100## [1] 0.4716981Str216.prev = Str216 %>%
count(Sample_group, SS_present) %>%
filter(SS_present==TRUE)
Sample_type_N <- Str216 %>%
count(Sample_group)%>%
rename(Total=n)%>%
filter(grepl('stgg|rna_protect', Sample_group))
Str216.prev$Total <- Sample_type_N$Total
Str216.prev$ASV <- "Streptococcus_216"
Str216.prev <- Str216.prev %>%
mutate(Prevalence = (n/Total)*100) %>%
mutate(Lower=as.numeric(c("11.71571", "4.11")))%>%
mutate(Upper=as.numeric(c("37.02429", "18.61")))%>%
select(Sample_group, n, Total, Prevalence, Lower, Upper, ASV)
str216.prop <- prop.test(x=Str216.prev$n, n=Str216.prev$Total, conf.level = 0.95, correct = FALSE)## Warning in prop.test(x = Str216.prev$n, n = Str216.prev$Total, conf.level =
## 0.95, : Chi-squared approximation may be incorrectstr216.prop##
## 2-sample test for equality of proportions without continuity
## correction
##
## data: Str216.prev$n out of Str216.prev$Total
## X-squared = 0.65186, df = 1, p-value = 0.4194
## alternative hypothesis: two.sided
## 95 percent confidence interval:
## -0.1834054 0.3691197
## sample estimates:
## prop 1 prop 2
## 0.1428571 0.0500000# Bar Plot Showing prevalence of Str216
ggplot(data = Str216.prev, aes(x=Sample_group, y=Prevalence, fill=Sample_group))+
geom_bar(stat = "identity", color="black", position=position_dodge()) +
geom_errorbar(aes(ymin=Prevalence-Lower, ymax=Prevalence+Upper), width=.2,
position=position_dodge(.9))+
scale_fill_manual(values = wes_palette(n=3, name = "FantasticFox1"))+
ylab("Prevalence (%)")+
xlab("Sample Type")+
ggtitle("Prevalence of Streptococcus_216 in STGG, RNA Protect
and Enivornmental Control Samples")#+
#ggsave("Prevanlence_Strep_Bar.tiff", units="in", width=6, height=5, dpi=300, compression = 'lzw')9.1.9 Streptococcus_303
Str303 <- filter(boxplot.SS_RA2, ASV == "Streptococcus_303")
sum(Str303$SS_present)## [1] 2sum(Str303$SS_present)/nrow(boxplot.SS_RA2)*100## [1] 0.4716981Str303.prev = Str303 %>%
count(Sample_group, SS_present) %>%
filter(SS_present==TRUE)
Sample_type_N <- Str303 %>%
count(Sample_group)%>%
rename(Total=n)%>%
filter(grepl('stgg', Sample_group))
Str303.prev$Total <- Sample_type_N$Total
Str303.prev$ASV <- "Streptococcus_303"
Str303.prev <- Str303.prev %>%
mutate(Prevalence = (n/Total)*100) %>%
mutate(Lower=as.numeric(c("7.21")))%>%
mutate(Upper=as.numeric(c("20.1")))%>%
select(Sample_group, n, Total, Prevalence, Lower, Upper, ASV)
# Bar Plot Showing prevalence of Str303
ggplot(data = Str303.prev, aes(x=Sample_group, y=Prevalence, fill=Sample_group))+
geom_bar(stat = "identity", color="black", position=position_dodge()) +
geom_errorbar(aes(ymin=Prevalence-Lower, ymax=Prevalence+Upper), width=.2,
position=position_dodge(.9))+
scale_fill_manual(values = wes_palette(n=3, name = "FantasticFox1"))+
ylab("Prevalence (%)")+
xlab("Sample Type")+
ggtitle("Prevalence of Streptococcus_303 in STGG, RNA Protect
and Enivornmental Control Samples")#+
#ggsave("Prevanlence_Strep_Bar.tiff", units="in", width=6, height=5, dpi=300, compression = 'lzw')9.1.10 Combined Bar Chart of All SS ASVs
boxplot.SS.prev <- bind_rows(Sta17.prev, Sta51.prev, Str34.prev, Str69.prev, Str90.prev, Str195.prev, Str216.prev, Str303.prev, .id = NULL) %>%
select(Sample_group, ASV, n, Prevalence, Lower, Upper)
print(xtable(boxplot.SS.prev, type = "latex", digits = 3), file = "SS_Prevalence_Table.tex")
# Bar Plot Showing prevalence
ggplot(data = boxplot.SS.prev, aes(x=ASV, y=Prevalence, fill=Sample_group))+
geom_bar(stat = "identity", color="black", position=position_dodge()) +
geom_errorbar(aes(ymin=Prevalence-Lower, ymax=Prevalence+Upper), width=.2,
position=position_dodge(.9))+
scale_fill_manual(name = "Sample Type", label = c("Blank", "RNA protect", "STGG"), values = wes_palette(n=3, name = "FantasticFox1"))+
ylab("Prevalence (%)")+
xlab("ASV")+
theme_bw()+
theme(axis.text.x = element_text(face = "italic"))+
#ggtitle("Prevalence of Streptococcus and Staphylococcus ASVs in STGG, RNA Protect and Enivornmental Control Samples")+
ggsave("Prevanlence_SS_ASV_Bar.pdf", units="in", width=12, height=5, dpi=300)
9.1.11 Table of Chi Squared
ChSq <- c(sta17.prop$statistic, str34.prop$statistic, sta51.prop$statistic, str69.prop$statistic, str90.prop$statistic, str195.prop$statistic, str216.prop$statistic, NA)
SS.prev_pval <- c(sta17.prop$p.value, str34.prop$p.value, sta51.prop$p.value, str69.prop$p.value, str90.prop$p.value, str195.prop$p.value, str216.prop$p.value, NA)
SS.prev_adjpval <- p.adjust(SS.prev_pval, method = "BH")
SS_names <- c("Staphylococcus_17", "Streptococcus_34", "Staphylococcus_51", "Streptococcus_69", "Streptococcus_90", "Streptococcus_195", "Streptococcus_216", "Streptococcus_303")
SS.prev.df <- data.frame(SS_names, ChSq, SS.prev_pval, SS.prev_adjpval)
print(xtable(SS.prev.df, type = "latex", digits = 3), file = "SS_Prevalence_Pval.tex")9.2 Relative Abundancee of Each Staph/Strep ASV
pd = position_dodge(width = 0.5)
ggplot(data = boxplot.SS_RA2, aes(x=ASV, y=log10_RA, fill=Sample_group))+
stat_boxplot(geom = "errorbar", position = pd, width = 0.25)+
geom_boxplot(width = 0.5, position = pd)+
scale_fill_manual(values = wes_palette(n=3, name = "FantasticFox1"))+
#geom_dotplot(binaxis = 'y', stackdir='center', dotsize = 1)+
#geom_jitter(shape=16, position=position_jitter(0.2))+
#ggtitle("Relative Abundance of Staphylococcus and Streptococcus ASVs
#in STGG, RNA protect and Environmental Controls")+
ylab("log10(Relative abundance)")+
scale_x_discrete(label=NULL)+
facet_wrap(~ASV, scales="free")+
theme_bw()+
ggsave("Relative_Abundance_SS_ASV_Bar.pdf", units="in", width=8, height=5, dpi=300)
#theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5), legend.position = "bottom")9.3 Statistical significance
# A series of linear models were used to compare relative abdundance of Streptococcus and STaphylococcus ASVs
RA.df_SS <- boxplot.SS_RA2
MiSeq_Run<-metadata_filtered%>%
select(Sample, Run_No) %>%
deframe()
RA.df_SS$MiSeq_Run<-MiSeq_Run[match(RA.df_SS$Sample.ID, names(MiSeq_Run))]9.3.0.1 Staphylococcus_17
sta17RA <- filter(RA.df_SS, ASV=="Staphylococcus_17")
sta17.lm <- lm(RA ~ Sample_group, data=sta17RA)
summary(sta17.lm)##
## Call:
## lm(formula = RA ~ Sample_group, data = sta17RA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.022779 -0.018956 -0.010740 0.001956 0.082973
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.019090 0.005149 3.708 0.000524 ***
## Sample_grouprna_protect 0.003689 0.011179 0.330 0.742787
## Sample_groupstgg -0.008350 0.007809 -1.069 0.290041
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02625 on 50 degrees of freedom
## Multiple R-squared: 0.0313, Adjusted R-squared: -0.007444
## F-statistic: 0.8079 on 2 and 50 DF, p-value: 0.4515par(mfrow=c(2,2))
plot(sta17.lm)
#### Streptococcus_34
str34RA <- filter(RA.df_SS, ASV=="Streptococcus_34")
str34.lm <- lm(RA ~ Sample_group, data=str34RA)
summary(str34.lm)##
## Call:
## lm(formula = RA ~ Sample_group, data = str34RA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.005817 -0.003372 -0.003372 -0.000897 0.076628
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.003372 0.002491 1.354 0.182
## Sample_grouprna_protect -0.002475 0.005409 -0.458 0.649
## Sample_groupstgg 0.002444 0.003778 0.647 0.521
##
## Residual standard error: 0.0127 on 50 degrees of freedom
## Multiple R-squared: 0.01749, Adjusted R-squared: -0.02181
## F-statistic: 0.445 on 2 and 50 DF, p-value: 0.6433par(mfrow=c(2,2))
plot(str34.lm)
#### Staphylococcus_51
sta51RA <- filter(RA.df_SS, ASV=="Staphylococcus_51")
sta51.lm <- lm(RA ~ Sample_group, data=sta51RA)
summary(sta51.lm)##
## Call:
## lm(formula = RA ~ Sample_group, data = sta51RA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.002575 -0.002575 -0.000684 -0.000684 0.048916
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0006839 0.0014227 0.481 0.633
## Sample_grouprna_protect -0.0006839 0.0030890 -0.221 0.826
## Sample_groupstgg 0.0018906 0.0021576 0.876 0.385
##
## Residual standard error: 0.007254 on 50 degrees of freedom
## Multiple R-squared: 0.02018, Adjusted R-squared: -0.01902
## F-statistic: 0.5148 on 2 and 50 DF, p-value: 0.6008par(mfrow=c(2,2))
plot(sta51.lm)
#### Streptococcus_69
str69RA <- filter(RA.df_SS, ASV=="Streptococcus_69")
str69.lm <- lm(RA ~ Sample_group, data=str69RA)
summary(str69.lm)##
## Call:
## lm(formula = RA ~ Sample_group, data = str69RA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.005725 -0.005725 -0.000229 -0.000229 0.065387
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.005725 0.002498 2.292 0.0261 *
## Sample_grouprna_protect -0.005725 0.005423 -1.056 0.2962
## Sample_groupstgg -0.005496 0.003788 -1.451 0.1531
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01274 on 50 degrees of freedom
## Multiple R-squared: 0.04801, Adjusted R-squared: 0.009934
## F-statistic: 1.261 on 2 and 50 DF, p-value: 0.2923par(mfrow=c(2,2))
plot(str69.lm)
#### Streptococcus_90
str90RA <- filter(RA.df_SS, ASV=="Streptococcus_90")
str90.lm <- lm(RA ~ Sample_group, data=str90RA)
summary(str90.lm)##
## Call:
## lm(formula = RA ~ Sample_group, data = str90RA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.006019 -0.006019 -0.000825 -0.000825 0.148343
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.006019 0.004204 1.432 0.158
## Sample_grouprna_protect -0.006019 0.009127 -0.659 0.513
## Sample_groupstgg -0.005195 0.006375 -0.815 0.419
##
## Residual standard error: 0.02143 on 50 degrees of freedom
## Multiple R-squared: 0.01674, Adjusted R-squared: -0.0226
## F-statistic: 0.4255 on 2 and 50 DF, p-value: 0.6558par(mfrow=c(2,2))
plot(str90.lm)
#### Streptococcus_195
str195RA <- filter(RA.df_SS, ASV=="Streptococcus_195")
str195.lm <- lm(RA ~ Sample_group, data=str195RA)
summary(str195.lm)##
## Call:
## lm(formula = RA ~ Sample_group, data = str195RA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0005118 -0.0005118 -0.0004359 -0.0004359 0.0108980
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.359e-04 4.142e-04 1.052 0.298
## Sample_grouprna_protect -4.359e-04 8.994e-04 -0.485 0.630
## Sample_groupstgg 7.587e-05 6.282e-04 0.121 0.904
##
## Residual standard error: 0.002112 on 50 degrees of freedom
## Multiple R-squared: 0.006242, Adjusted R-squared: -0.03351
## F-statistic: 0.157 on 2 and 50 DF, p-value: 0.8551par(mfrow=c(2,2))
plot(str195.lm)
#### Streptococcus_216
str216RA <- filter(RA.df_SS, ASV=="Streptococcus_216")
str216.lm <- lm(RA ~ Sample_group, data=str216RA)
summary(str216.lm)##
## Call:
## lm(formula = RA ~ Sample_group, data = str216RA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0003901 -0.0002419 0.0000000 0.0000000 0.0045964
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.446e-20 1.484e-04 0.000 1.000
## Sample_grouprna_protect 3.901e-04 3.222e-04 1.211 0.232
## Sample_groupstgg 2.419e-04 2.251e-04 1.075 0.288
##
## Residual standard error: 0.0007567 on 50 degrees of freedom
## Multiple R-squared: 0.03877, Adjusted R-squared: 0.0003211
## F-statistic: 1.008 on 2 and 50 DF, p-value: 0.3721par(mfrow=c(2,2))
plot(str216.lm)
#### Streptococcus_301 Only found in one sample group so no linear model required
9.3.0.2 Adjusted p values
lmp <- function (modelobject) {
if (class(modelobject) != "lm") stop("Not an object of class 'lm' ")
f <- summary(modelobject)$fstatistic
p <- pf(f[1],f[2],f[3],lower.tail=F)
attributes(p) <- NULL
return(p)
}
SS.RA_pval <- c(lmp(sta17.lm), lmp(str34.lm), lmp(sta51.lm), lmp(str69.lm), lmp(str90.lm), lmp(str195.lm), lmp(str216.lm), NA)
SS.RA_adjpval <- p.adjust(SS.RA_pval, method = "BH")
SS.RA_R2 <- c(summary(sta17.lm)$r.squared, summary(str34.lm)$r.squared, summary(sta51.lm)$r.squared, summary(str69.lm)$r.squared, summary(str90.lm)$r.squared, summary(str195.lm)$r.squared, summary(str216.lm)$r.squared, NA)
SS.RA_table <- data.frame(SS_names, SS.RA_R2, SS.RA_pval, SS.RA_adjpval)
print(xtable(SS.RA_table, type = "latex", digits = 3), file = "SS_Relative_Abundance_Linear_Model.tex")10 Random Forest
# set random seed number
set.seed(151)
library(randomForest)
# run model
rf1 <- randomForest(t(otu_table_ord_RA), as.factor(metadata_filtered$Sample_type), ntree=1000)
# retreive OTU importance from model
imp <- data.frame(predictors = rownames(importance(rf1)), importance(rf1))
# Order the predictor levels by importance
imp.sort <- arrange(imp, desc(MeanDecreaseGini))
imp.sort$predictors <- factor(imp.sort$predictors, levels = imp.sort$predictors)
# Select the top 10 predictors
imp.10 <- imp.sort[1:20, ]
# plot otus by decreasing importance
ggplot(imp.10, aes(x = predictors, y = MeanDecreaseGini)) +geom_bar(stat = "identity", fill = "indianred") +coord_flip()+
theme_bw()